CN111665533A - Positioning method/system, medium, and apparatus based on satellite positioning validity - Google Patents
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Abstract
The invention discloses a combined positioning method/system, a storage medium and equipment based on satellite positioning effectiveness.
Description
Technical Field
The present invention relates to the field of positioning technologies, and in particular, to a combined positioning method/system, a storage medium, and a device based on satellite positioning validity.
Background
Positioning technologies can be divided into indoor positioning and outdoor positioning. Currently, the common positioning technologies for outdoor positioning include satellite positioning technologies such as GPS and beidou, which can meet most outdoor positioning requirements, but satellite signals cannot reach indoors due to building shielding, and if the satellite technology is still directly used for positioning indoors, the positioning accuracy is greatly reduced; in the indoor positioning technologies, Wi-Fi fingerprint positioning, Zigbee (Ultra wide band), Pedestrian Dead Reckoning (PDR), and the like can realize indoor positioning with higher precision, but the technologies all depend on facilities deployed in advance, so that a large amount of cost is required, and the indoor positioning technologies are difficult to play when an emergency occurs; only PDR positioning does not depend on prior equipment deployment, but PDR can only provide short-time indoor high-precision positioning, and the position information provided by PDR is relative position information.
Theoretically, after satellite positioning and PDR are combined, outdoor positioning and indoor positioning can be achieved, that is, absolute position information provided by satellite positioning is used as an initial reference position of PDR, but in practical application, when the PDR is in a half-shielding state or enters the PDR from the outdoor, the precision of satellite positioning may be reduced, and the positioning precision after entering the PDR or in the half-shielding state cannot be guaranteed; in addition, even if the positioning module (such as intelligent wearable equipment and a mobile phone) is carried by a pedestrian outdoors, the body is likely to interfere with the receiving model of the positioning module, and the positioning accuracy is reduced.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, it is a primary object of the present invention to provide a combined positioning method/system, storage medium and device based on satellite positioning effectiveness, which can improve positioning accuracy.
In order to achieve the above objects and other related objects, the technical solution of the present invention is as follows:
a combined positioning method based on satellite positioning effectiveness comprises the following steps:
acquiring satellite positioning data and PDR positioning data at a specified frequency;
extracting classification features used for satellite positioning effectiveness identification in the satellite positioning data and the PDR positioning data;
judging the validity of the current satellite positioning data corresponding to the current moment according to the classification features corresponding to the current moment; if the current satellite positioning data is valid, fusing the satellite positioning data and the PDR data to calculate a current output position;
and if the current satellite positioning data is invalid, calculating the current output position by taking the previous output position as the initial position of the PDR.
Optionally, the classification characteristic includes one or more of a displacement difference between satellite positioning and PDR positioning, a heading angle difference between satellite positioning and PDR positioning, a geometric precision factor of satellite positioning, a number of visible satellites, a satellite received signal strength, a satellite received noise ratio, a minimum satellite cut-off angle, and a satellite azimuth.
Optionally, when the validity of the current satellite positioning data corresponding to the current time is judged, the classification features corresponding to the current time are input into a pre-constructed satellite positioning validity recognition classifier, and the satellite positioning validity recognition classifier includes a support vector machine model.
Optionally, the constructing of the satellite positioning validity identification classifier includes:
constructing a training set and a test set by acquiring the satellite positioning data and the PDR positioning data,
inputting the training set into the support vector machine model for training, and optimizing the support vector machine model;
inputting the test set into the optimized support vector machine model, and evaluating classification results by using a cross method.
Optionally, if the current satellite positioning data is invalid, the formula for calculating the current output position by using the previous final position as the initial position of the PDR is as follows:
Sk＝Sk-1+ΔS
where k denotes the current time, k-1 denotes the previous time, SkIndicating the current output position, Sk-1Representing a previous output position, Δ S from a previous time k-1 to a current time k calculated from said PDR positioning dataA corresponding displacement.
Optionally, the method for calculating and outputting the current output position by fusing the satellite positioning data and the PDR data by using a kalman filtering method includes:
solving a current posterior position error quantity by adopting a Kalman filtering method, wherein the position error quantity comprises a displacement error, a step error and a course angle deviation error;
and calculating the current output position according to the error amount of the current posterior position.
Optionally, the position error amount is expressed as:
X＝[E,N,Le,Be]
wherein X represents a position error amount,Eindicating a displacement error in a first direction;Nindicating a displacement error in a second direction, the second direction being perpendicular to the first direction; l iseRepresenting the step estimation error; b iseIndicating a heading angle error.
Optionally, the first direction includes a true east direction, and the second direction correspondingly includes a true north direction.
Optionally, the method for solving the current a posteriori position error amount includes:
according to the PDR positioning data, a state system model is built, and the current prior certificate position error quantity is calculated;
according to the satellite positioning data and the PDR positioning data, constructing a deviation amount of an observation system model, and calculating a current measurement position error amount;
calculating a current prior position error amount covariance matrix according to the PDR positioning data and a pre-calculated previous posterior position error amount covariance matrix;
calculating a current Kalman gain according to the current prior position error covariance matrix;
calculating the current posterior position error amount according to the current prior position error amount, the current measurement position error amount and the current Kalman gain;
and generating a covariance matrix of the error amount of the current posterior position according to the covariance matrix of the error amount of the current Kalman gain and the error amount of the current prior position.
Optionally, the state system model is:
X′k＝φk,k-1Xk-1
wherein k represents the current time, k-1 represents the previous time, X'kRepresenting the amount of current a priori position error, phik,k-1Representing a state transition matrix, Xk-1The previous a posteriori position error amount, the state transition matrix phik,k-1Expressed as:
in the state transition matrix, θk-1Indicating the course angle estimation error at the previous time, Lk-1Indicating the step estimation error, T, at the previous instantpA time constant representing that the step error conforms to a first order Markov process; t isbRepresenting that the course error conforms to a relevant time constant of a first-order Markov process, T representing a sampling interval, and n representing the number of walking steps in the sampling interval;
the observation system model is as follows:
Zk＝HkX′+Vk
wherein Z iskRepresenting a current observed position error amount, the current observed position error amount being a position deviation between a current satellite positioning position obtained from the satellite positioning data and a PDR positioning position obtained from the PDR positioning data; hkRepresenting an observation matrix, VkRepresenting measurement noise, the observation matrix HkExpressed as:
the calculation formula of the covariance matrix of the current prior error quantity is as follows:
wherein,
representing the covariance matrix, P, of the error measure of the current prior positionk-1A previous a posteriori position error amount covariance matrix is represented,
a transposed matrix, Q, representing said state transition matrixNRepresenting a process noise covariance matrix;
the calculation formula of the current Kalman gain is as follows:
wherein, KkRepresenting the current Kalman gain, RQRepresenting a measurement noise covariance matrix;
the calculation formula of the current posterior position error amount is as follows:
Xk＝X′k+Kk(Zk-HkX′k)
wherein, XkRepresenting the current a posteriori position error amount;
the formula for updating the error covariance matrix is:
wherein, PkRepresenting the covariance matrix of the error amount of the current a posteriori position.
Optionally, a calculation formula for calculating the current output position according to the current posterior position error amount is as follows:
Sk＝Sgps+Xk
wherein S iskRepresenting said current output position, SgpsRepresenting the current position obtained from the satellite positioning.
A combined positioning system based on satellite positioning effectiveness, comprising:
the data acquisition module is used for acquiring satellite positioning data and PDR positioning data at a specified frequency;
the classification feature extraction module is used for extracting classification features used for satellite positioning effectiveness identification in the satellite positioning data and the PDR positioning data;
the satellite positioning effectiveness identification classifier is used for judging the effectiveness of the current satellite positioning data corresponding to the current moment according to the classification features corresponding to the current moment;
the current output position calculation module is used for calculating a current output position according to a judgment result of the satellite positioning effective identification classifier, and comprises a first calculation submodule and a second calculation submodule;
if the current satellite positioning data is valid, the first calculation sub-module adopts a Kalman filtering method to fuse the satellite positioning data and the PDR data to calculate the current output position,
and if the current satellite positioning data is invalid, the second calculation submodule calculates the current output position by taking the previous output position as the initial position of the PDR.
Optionally, the classification characteristic includes one or more of a displacement difference between satellite positioning and PDR positioning, a heading angle difference between satellite positioning and PDR positioning, a geometric precision factor of satellite positioning, a number of visible satellites, a satellite received signal strength, a satellite received noise ratio, a minimum satellite cut-off angle, and a satellite azimuth.
Optionally, the satellite positioning effectiveness identification classifier includes a support vector machine model.
Optionally, the first computing submodule includes:
the current posterior position error amount calculation unit is used for fusing the satellite positioning data and the PDR data according to a Kalman filtering method to obtain a current posterior position error amount, and the position error amount comprises a displacement error, a step error and a course angle deviation error;
and the current output position correcting unit is used for correcting the current satellite positioning data according to the current posterior error to obtain the current output position.
Optionally, the current posterior position error amount calculating unit includes:
a current prior position error amount calculation subunit for calculating a current prior verified position error amount from the position error amount of the PDR positioning data;
a current measurement position error amount calculation subunit for calculating a current measurement position error amount from the satellite positioning data and the deviation amount of the PDR positioning data;
the current prior position error amount covariance matrix calculation subunit is used for calculating a current prior position error amount covariance matrix according to the PDR positioning data and a pre-calculated previous posterior position error amount covariance matrix;
a current Kalman gain calculation subunit, configured to calculate a current Kalman gain according to the current prior position error covariance matrix;
a current posterior position error amount calculation subunit, configured to calculate the current posterior position error amount according to the current prior position error amount, the current measurement position error amount, and a current kalman gain;
and the current posterior position error amount covariance matrix generation subunit is used for generating a current posterior position error amount covariance matrix according to the current Kalman gain and the current prior position error amount covariance matrix.
A storage medium having stored thereon a computer program which, when executed by a processor, implements a combined positioning method based on satellite positioning effectiveness for an indoor mobile body as any one of the above.
An apparatus comprising a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program stored by the memory to cause the apparatus to perform any of the combined positioning methods based on satellite positioning effectiveness described above.
According to the combined positioning method/system, the storage medium and the equipment based on the satellite positioning effectiveness, the effectiveness judgment is carried out on the collected current satellite positioning data before the position estimation, different calculation modes are adopted according to the effectiveness judgment result, the problems of continuous distortion and step jump of the satellite positioning are solved, the effective utilization rate of the satellite positioning is improved, and the positioning precision is improved.
Drawings
FIG. 1 is a flow chart of a combined positioning method based on satellite positioning effectiveness according to the present invention;
FIG. 2 is a flow chart illustrating a calculation of a current output position by Kalman filtering according to the present invention;
FIG. 3 is a block diagram of a combined positioning system based on satellite positioning effectiveness according to the present invention;
fig. 4 is a block diagram showing the structure of a first computing submodule in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below with reference to the accompanying drawings. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention.
It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Referring to fig. 1, the combined positioning method based on satellite positioning effectiveness provided in this embodiment includes:
and step 180B, if the current satellite positioning data is invalid, calculating the current output position by taking the previous output position as the initial position of the PDR.
In this embodiment, the satellite positioning data may be any one of satellite data, for example: GPS satellite navigation data or beidou positioning data.
In this embodiment, the designated frequency may be any frequency, but the higher the frequency, the higher the positioning accuracy, and the larger the data amount.
In the combined positioning method based on satellite positioning effectiveness of the embodiment, before the position estimation, the effectiveness judgment is performed on the collected current satellite positioning data, when the satellite positioning data is effective, the Kalman filtering method is used for combining the data of the satellite positioning and the PDR positioning to correct the current output position, so that the satellite positioning data is corrected, the output position is more accurate, and under the condition that the satellite positioning data is in an outdoor channel or is in a semi-shielding state, if the satellite positioning data is changed from effective to invalid, at the moment that the satellite positioning data is changed from effective to invalid, the output position is obtained by combining the effective satellite positioning data at the previous moment with the PDR positioning data, and the subsequent output positions are obtained by combining the effective positioning data at the moment with the PDR positioning data, so that the seamless positioning from outdoor to indoor is realized, the problems of continuous distortion and step jump of the satellite positioning are solved, and the effective utilization rate, the positioning accuracy is improved.
In this embodiment, the classification features include a displacement difference between satellite positioning and PDR positioning, a heading angle difference between satellite positioning and PDR positioning, a geometric precision factor of satellite positioning, a number of visible satellites, a satellite received signal strength, a satellite received noise ratio, a minimum satellite cut-off angle, and a satellite azimuth. In actual implementation, the classification feature may include one or more of these features.
Wherein, the displacement difference of satellite positioning and PDR location means that in same time span, the difference of the corresponding displacement of satellite positioning and the corresponding displacement of PDR location compares satellite positioning data, and the precision of PDR location is higher, and the step length of PDR position calculation can reach centimetre level, consequently, can judge whether effective satellite positioning data is based on the size of this displacement difference, if the displacement difference is too big, then can judge that satellite positioning data is invalid.
The course angle difference between satellite positioning and PDR positioning refers to the difference between the corresponding deflection angle of satellite positioning and the corresponding deflection angle of PDR positioning in the same time range, when the PDR positioning algorithm calculates the deflection angle, the PDR positioning algorithm can be obtained by fusing gyroscope data according to the deflection angle calculated by an accelerometer and a magnetometer, and PDR positioning can obtain course angle data which is more accurate than satellite positioning, so that whether satellite positioning data are effective or not can be judged based on the magnitude of the course angle difference, and if the course angle difference is too large, the satellite positioning data can be judged to be invalid.
Under the semi-shielding scene, the direction of the GPS output has a drift problem. The angle fusion algorithm of the PDR algorithm is to further fuse gyroscope data on the basis that the accelerometer and the magnetometer resolve the heading angle, so that the heading angle output by the PDR algorithm is more accurate than the angle output by the electronic compass alone. Therefore, the angular difference between the GPS and PDR outputs can reflect the effectiveness of the GPS.
Wherein, the Geometric distribution of Precision (GDOP) of the satellite is used to measure the influence of the space Geometric distribution of the observation satellite on the positioning Precision, and the calculation formula of the GDOP can be expressed as:
GDOP＝ΔP/ΔM
in the GDOP calculation formula, Δ M is the variation of the measurement result, Δ P is the variation of the geometric position, and GDOP represents the degree of sensitivity to errors.
The number of visible satellites can directly reflect whether the environment of a signal receiving device (such as wearing equipment and a mobile phone) is shielded or not to a certain extent, and generally, the more the number of visible satellites is, the higher the corresponding positioning accuracy is.
The lower the satellite received signal strength is, the lower the accuracy of the satellite positioning data is, and the lower the satellite received signal to noise ratio is, the lower the reliability of the satellite positioning data is.
The satellite cut-off angle is larger in the shielded environment than in the open environment, the low satellite cut-off angle is more likely to cause the multipath effect of signals, the satellite cut-off angle is equivalent to an angle threshold, visible satellites higher than the threshold can be used for calculating the position, satellites lower than the threshold cannot participate in position settlement, the positioning accuracy can be influenced to a certain extent by the size of the threshold aiming at different scenes, and the lowest satellite cut-off angle is the lower limit of the threshold. Therefore, the lowest satellite cut-off angle caused by the occlusion can be used as an aid to determine the validity of the satellite positioning.
In this embodiment, when the validity of the current satellite positioning data corresponding to the current time is determined, the classification features corresponding to the current time are input into a pre-constructed satellite positioning validity recognition classifier, which may be a support vector machine model, and in a specific implementation process, other types of satellite positioning validity classifiers may also be used.
In some embodiments, the method for constructing the satellite positioning validity recognition classifier may include:
constructing a training set and a test set by acquiring the satellite positioning data and the PDR positioning data,
inputting the training set into the support vector machine model for training, and optimizing the support vector machine model;
inputting the test set into the optimized support vector machine model, and evaluating classification results by using a cross method.
The method for optimizing the support vector machine model comprises the following steps:
a kernel function is selected, and in this embodiment, the kernel function is selected as a polynomial kernel function, that is:
M(x,y)＝(1+xy)d
defining the splitting plane as a hyperplane, which can be expressed as:
wTx+b
the distance of the sample point to the hyperplane is then:
|wTx+b|/||w||
to calculate w and b, the maximum classification interval needs to be solved:
by introducing lagrange multipliers, the original problem is converted into a dual problem:
wherein the dual problem satisfies an inequality constraint:
0≤αi≤C
∑αi·l(i)＝0
in some embodiments, if the current satellite positioning data is invalid, the formula for calculating the current output position with the previous final position as the initial position of the PDR is:
Sk＝Sk-1+ΔS
where k denotes the current time, k-1 denotes the previous time, SkIndicating the current output position, Sk-1And representing the previous output position, and calculating the displacement corresponding to the previous time k-1 to the current time k according to the PDR positioning data by the deltaS.
In some embodiments, referring to fig. 2, the method for computing and outputting the current output position by fusing the satellite positioning data and the PDR data through the kalman filtering method includes:
solving a current posterior position error quantity by adopting a Kalman filtering method, wherein the position error quantity comprises a displacement error, a step error and a course angle deviation error;
and calculating the current output position according to the error amount of the current posterior position.
In some embodiments, the position error amount is expressed as:
X＝[E,N,Le,Be]
wherein X represents a position error amount,Eindicating a displacement error in a first direction;Nindicating a displacement error in a second direction, the second direction being perpendicular to the first direction; l iseRepresenting the step estimation error; b iseIndicating a heading angle error. In some embodiments, the first direction comprises a true east direction and the second direction correspondence comprises a true north direction.
In some embodiments, referring to fig. 2, the method of solving for the current a posteriori position error amount comprises:
and 186, generating a covariance matrix of the error amount of the current posterior position according to the covariance matrix of the error amount of the current Kalman gain and the error amount of the current prior position.
In actual implementation, the above steps are performed in a loop, and the previous a-posteriori position error amount in step 183 is calculated by step 186 in the previous loop calculation.
In the above and following calculation processes, the current prior position error amount, the current measured position error amount, and the current posterior position error amount are position error amounts, and the expression modes thereof can be expressed by a matrix formed by a displacement error, a step error, and a course angle deviation error, that is, X ═ 2E,N,Le,Be]。
In some embodiments, specifically, in step 181, the state system model may be:
X′k＝φk,k-1Xk-1
wherein k represents the current time, k-1 represents the previous time, X'kRepresenting the amount of current a priori position error, phik,k-1Representing a state transition matrix, Xk-1The previous a posteriori position error amount, the state transition matrix phik,k-1Expressed as:
in the state transition matrix, θk-1Indicating the course angle estimation error at the previous time, Lk-1Indicating the step estimation error, T, at the previous instantpA time constant representing that the step error conforms to a first order Markov process; t isbThe heading error is expressed to accord with a relevant time constant of a first-order Markov process, T represents a sampling interval, T can be set to be 1, T can also be selected from other arbitrary time lengths, and n represents the walking step number in the sampling interval;
in step 182, the observation system model may be:
Zk＝HkX′+Vk
wherein Z iskRepresenting a current observed position error amount, the current observed position error amount being a position deviation between a current satellite positioning position obtained from the satellite positioning data and a PDR positioning position obtained from the PDR positioning data; hkRepresenting an observation matrix, VkRepresenting measurement noise, the observation matrix HkExpressed as:
in step 183, the calculation formula of the covariance matrix of the current prior error amount may be:
wherein,
representing the covariance matrix, P, of the error measure of the current prior positionk-1A previous a posteriori position error amount covariance matrix is represented,
a transposed matrix, Q, representing said state transition matrixNRepresenting a process noise covariance matrix;
in step 184, the calculation formula of the current kalman gain may be:
wherein, KkRepresenting the current Kalman gain, RQRepresenting a measurement noise covariance matrix;
in step 185, the formula for calculating the current a-posteriori position error amount may be:
Xk＝X′k+Kk(Zk-HkX′k)
wherein, XkRepresenting the current a posteriori position error amount;
in step 186, the formula for updating the error covariance matrix may be:
wherein, PkRepresenting the covariance matrix of the error amount of the current a posteriori position.
Then, in step 187, the calculation formula for calculating the current output position according to the current posterior position error amount may be:
Sk＝Sgps+Xk
wherein S iskRepresenting said current output position, SgpsRepresenting the current position obtained from the satellite positioning.
The present embodiment also provides a storage medium having a computer program stored thereon, which when executed by a processor, implements any of the above-described methods for combined positioning of indoor mobile bodies based on satellite positioning effectiveness.
The storage medium in this embodiment can be understood by those skilled in the art as follows: all or a portion of the steps for implementing the method embodiments of the present description may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. When executed, performs steps comprising method embodiments of the present specification; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Referring to fig. 3, the present embodiment further discloses a combined positioning system based on satellite positioning effectiveness corresponding to the combined positioning method based on satellite positioning effectiveness, where the combined positioning system includes:
a data acquisition module 2 for acquiring satellite positioning data and PDR positioning data at a specified frequency;
a classification feature extraction module 4, configured to extract a classification feature used for satellite positioning validity identification in the satellite positioning data and the PDR positioning data;
the satellite positioning effectiveness identification classifier 6 is used for judging the effectiveness of the current satellite positioning data corresponding to the current moment according to the classification characteristics corresponding to the current moment; ,
a current output position calculation module 8, configured to calculate a current output position according to a determination result of the satellite positioning effective identification classifier, where the current output position calculation module includes a first calculation submodule 81 and a second calculation submodule 82;
if the current satellite positioning data is valid, the first calculating sub-module 81 calculates the current output position by fusing the satellite positioning data and the PDR data by using a kalman filter method,
if the current satellite positioning data is invalid, the second calculation sub-module 82 calculates the current output position using the previous output position as the initial position of the PDR.
In some embodiments, the classification characteristic includes one or more of a displacement difference between the satellite positioning and the PDR positioning, a heading angle difference between the satellite positioning and the PDR positioning, a satellite positioning geometric precision factor, a number of visible satellites, a satellite received signal strength, a satellite received signal-to-noise ratio, a minimum satellite cut-off angle, and a satellite azimuth.
In some embodiments, the satellite positioning validity identification classifier comprises a support vector machine model.
In some embodiments, referring to fig. 4, the first calculation submodule 81 includes:
a current posterior position error amount calculation unit 81a, configured to obtain a current posterior position error amount by fusing the satellite positioning data and the PDR data according to a kalman filtering method, where the position error amount includes a displacement error, a step error, and a course angle deviation error;
and the current output position correcting unit 81b is used for correcting the current satellite positioning data according to the current posterior error to obtain a current output position.
In some embodiments, referring to fig. 4, the current a posteriori position error amount calculation unit 81a includes:
a current prior position error amount sub-unit 811 for calculating a current prior position error amount from the position error amount of the PDR positioning data;
a current measurement position error amount calculation subunit 812 for calculating a current measurement position error amount from the deviation amount of the satellite positioning data and the PDR positioning data;
a current prior position error amount covariance matrix calculation subunit 813 configured to calculate a current prior position error amount covariance matrix according to the PDR positioning data and a pre-calculated previous posterior position error amount covariance matrix;
a current kalman gain calculating subunit 814, configured to calculate a current kalman gain according to the current prior position error covariance matrix;
a current posterior position error amount calculation sub-unit 815 for calculating the current posterior position error amount according to the current prior position error amount, the current measurement position error amount, and a current kalman gain;
a current posterior position error amount covariance matrix generation subunit 816 is configured to generate a current posterior position error amount covariance matrix according to the current kalman gain and the current prior position error amount covariance matrix.
The invention also provides a device comprising a processor and a memory, wherein the memory is used for storing a computer program, and the processor is used for executing the computer program stored by the memory, so that the device can execute any one of the combined positioning methods based on satellite positioning effectiveness.
The present embodiment provides an apparatus, which includes a processor, a memory, a transceiver, and a communication interface, the memory and the communication interface are connected to the processor and the transceiver and perform mutual communication, the memory is used for storing a computer program, the communication interface is used for performing communication, and the processor and the transceiver are used for executing the computer program, so that the apparatus performs the steps of the combined positioning method based on satellite positioning effectiveness.
In this embodiment, the Memory may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (18)
1. A combined positioning method based on satellite positioning effectiveness is characterized by comprising the following steps:
acquiring satellite positioning data and PDR positioning data at a specified frequency;
extracting classification features used for satellite positioning effectiveness identification in the satellite positioning data and the PDR positioning data;
judging the validity of the current satellite positioning data corresponding to the current moment according to the classification features corresponding to the current moment;
if the current satellite positioning data is valid, fusing the satellite positioning data and the PDR data to calculate the current output position,
and if the current satellite positioning data is invalid, calculating the current output position by taking the previous output position as the initial position of the PDR.
2. A combined positioning method based on satellite positioning effectiveness according to claim 1, characterized in that: the classification characteristics comprise one or more of displacement difference between satellite positioning and PDR positioning, course angle difference between satellite positioning and PDR positioning, geometric precision factor of satellite positioning, visible satellite number, satellite receiving signal intensity, satellite receiving noise ratio, lowest satellite cut-off angle and satellite azimuth angle.
3. The combined positioning method based on satellite positioning effectiveness according to claim 1, characterized in that when the effectiveness of the current satellite positioning data corresponding to the current time is judged, the classification features corresponding to the current time are input into a pre-constructed satellite positioning effectiveness recognition classifier, and the satellite positioning effectiveness recognition classifier comprises a support vector machine model.
4. A combined positioning method based on satellite positioning effectiveness according to claim 3, characterized in that: the construction method of the satellite positioning effectiveness identification classifier comprises the following steps:
constructing a training set and a test set by acquiring the satellite positioning data and the PDR positioning data,
inputting the training set into the support vector machine model for training, and optimizing the support vector machine model;
inputting the test set into the optimized support vector machine model, and evaluating classification results by using a cross method.
5. A combined positioning method based on satellite positioning effectiveness according to claim 1, characterized in that: if the current satellite positioning data is invalid, the formula for calculating the current output position by taking the previous final position as the initial position of the PDR is as follows:
Sk＝Sk-1+ΔS
where k denotes the current time, k-1 denotes the previous time, SkIndicating the current output position, Sk-1And representing the previous output position, and calculating the displacement corresponding to the previous time k-1 to the current time k according to the PDR positioning data by the deltaS.
6. A combined positioning method based on satellite positioning effectiveness according to claim 1, characterized in that: the method for fusing the satellite positioning data and the PDR data to calculate and output the current output position by adopting a Kalman filtering method comprises the following steps:
solving a current posterior position error quantity by adopting a Kalman filtering method, wherein the position error quantity comprises a displacement error, a step error and a course angle deviation error;
and calculating the current output position according to the error amount of the current posterior position.
7. The combined positioning method based on satellite positioning effectiveness according to claim 6, characterized in that:
the position error amount is expressed as:
X＝[E,N,Le,Be]
wherein X represents a position error amount,Eindicating a displacement error in a first direction;Nindicating a displacement error in a second direction, the second direction being perpendicular to the first direction; l iseRepresenting the step estimation error; b iseIndicating a heading angle error.
8. The combined positioning method based on satellite positioning effectiveness according to claim 7, characterized in that: the first direction comprises a true east direction, and the second direction correspondingly comprises a true north direction.
9. The combined positioning method based on satellite positioning effectiveness according to claim 7, characterized in that:
the method for solving the current posterior position error amount comprises the following steps:
according to the PDR positioning data, a state system model is built, and the current prior certificate position error quantity is calculated;
according to the satellite positioning data and the PDR positioning data, constructing a deviation amount of an observation system model, and calculating a current measurement position error amount;
calculating a current prior position error amount covariance matrix according to the PDR positioning data and a pre-calculated previous posterior position error amount covariance matrix;
calculating a current Kalman gain according to the current prior position error covariance matrix;
calculating the current posterior position error amount according to the current prior position error amount, the current measurement position error amount and the current Kalman gain;
and generating a covariance matrix of the error amount of the current posterior position according to the covariance matrix of the error amount of the current Kalman gain and the error amount of the current prior position.
10. A combined positioning method based on satellite positioning effectiveness according to claim 9, characterized in that:
the state system model is as follows:
X'k＝φk,k-1Xk-1
wherein k represents the current time, k-1 represents the previous time, X'kRepresenting the amount of current a priori position error, phik,k-1Representing a state transition matrix, Xk-1The previous a posteriori position error amount, the state transition matrix phik,k-1Expressed as:
in the state transition matrix, θk-1Indicating the course angle estimation error at the previous time, Lk-1Indicating the step estimation error, T, at the previous instantpA time constant representing that the step error conforms to a first order Markov process; t isbRepresenting that the course error conforms to a relevant time constant of a first-order Markov process, T representing a sampling interval, and n representing the number of walking steps in the sampling interval;
the observation system model is as follows:
Zk＝HkX′+Vk
wherein Z iskRepresenting a current observed position error amount, the current observed position error amount being a current satellite fix obtained from the satellite positioning dataA position deviation between a bit position and a PDR position location obtained from the PDR position location data; hkRepresenting an observation matrix, VkRepresenting measurement noise, the observation matrix HkExpressed as:
the calculation formula of the covariance matrix of the current prior error quantity is as follows:
wherein,
representing the covariance matrix, P, of the error measure of the current prior positionk-1A previous a posteriori position error amount covariance matrix is represented,
a transposed matrix, Q, representing said state transition matrixNRepresenting a process noise covariance matrix;
the calculation formula of the current Kalman gain is as follows:
wherein, KkRepresenting the current Kalman gain, RQRepresenting a measurement noise covariance matrix;
the calculation formula of the current posterior position error amount is as follows:
Xk＝X'k+Kk(Zk-HkX'k)
wherein, XkRepresenting the current a posteriori position error amount;
the formula for updating the error covariance matrix is:
wherein, PkRepresenting the covariance matrix of the error amount of the current a posteriori position.
11. A combined positioning method based on satellite positioning effectiveness according to claim 10, characterized in that:
the calculation formula for calculating the current output position according to the error amount of the current posterior position is as follows:
Sk＝Sgps+Xk
wherein S iskRepresenting said current output position, SgpsRepresenting the current position obtained from the satellite positioning.
12. A combined positioning system based on satellite positioning effectiveness, comprising:
the data acquisition module is used for acquiring satellite positioning data and PDR positioning data at a specified frequency;
the classification feature extraction module is used for extracting classification features used for satellite positioning effectiveness identification in the satellite positioning data and the PDR positioning data;
the satellite positioning effectiveness identification classifier is used for judging the effectiveness of the current satellite positioning data corresponding to the current moment according to the classification features corresponding to the current moment;
the current output position calculation module is used for calculating a current output position according to a judgment result of the satellite positioning effective identification classifier, and comprises a first calculation submodule and a second calculation submodule;
if the current satellite positioning data is valid, the first calculation sub-module adopts a Kalman filtering method to fuse the satellite positioning data and the PDR data to calculate the current output position,
and if the current satellite positioning data is invalid, the second calculation submodule calculates the current output position by taking the previous output position as the initial position of the PDR.
13. A combined positioning system based on satellite positioning effectiveness according to claim 12, characterized in that: the classification characteristics comprise one or more of displacement difference between satellite positioning and PDR positioning, course angle difference between satellite positioning and PDR positioning, geometric precision factor of satellite positioning, visible satellite number, satellite receiving signal intensity, satellite receiving noise ratio, lowest satellite cut-off angle and satellite azimuth angle.
14. A combined positioning system based on satellite positioning effectiveness according to claim 12, characterized in that: the satellite positioning effectiveness identification classifier comprises a support vector machine model.
15. A combined positioning system based on satellite positioning effectiveness according to claim 12, characterized in that the first calculation submodule comprises:
the current posterior position error amount calculation unit is used for fusing the satellite positioning data and the PDR data according to a Kalman filtering method to obtain a current posterior position error amount, and the position error amount comprises a displacement error, a step error and a course angle deviation error;
and the current output position correcting unit is used for correcting the current satellite positioning data according to the current posterior error to obtain the current output position.
16. A combined positioning system based on satellite positioning effectiveness according to claim 15, characterized in that: the current posterior position error amount calculation unit includes:
a current prior position error amount calculation subunit for calculating a current prior verified position error amount from the position error amount of the PDR positioning data;
a current measurement position error amount calculation subunit for calculating a current measurement position error amount from the satellite positioning data and the deviation amount of the PDR positioning data;
the current prior position error amount covariance matrix calculation subunit is used for calculating a current prior position error amount covariance matrix according to the PDR positioning data and a pre-calculated previous posterior position error amount covariance matrix;
a current Kalman gain calculation subunit, configured to calculate a current Kalman gain according to the current prior position error covariance matrix;
a current posterior position error amount calculation subunit, configured to calculate the current posterior position error amount according to the current prior position error amount, the current measurement position error amount, and a current kalman gain;
and the current posterior position error amount covariance matrix generation subunit is used for generating a current posterior position error amount covariance matrix according to the current Kalman gain and the current prior position error amount covariance matrix.
17. A storage medium having a computer program stored thereon, characterized in that: the program is executed by a processor to realize a combined positioning method based on satellite positioning effectiveness for an indoor mobile unit according to any one of claims 1 to 11.
18. An apparatus, characterized by: comprising a processor and a memory, the memory being adapted to store a computer program, the processor being adapted to execute the computer program stored by the memory to cause the apparatus to perform the combined positioning method based on satellite positioning availability of any of claims 1-11.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112416015A (en) * | 2020-11-11 | 2021-02-26 | 国网安徽省电力有限公司淮南供电公司 | Power tower inspection unmanned aerial vehicle anti-lost control method and system |
| CN115469344A (en) * | 2022-06-10 | 2022-12-13 | 北京罗克维尔斯科技有限公司 | Vehicle positioning method and device, vehicle, electronic equipment and storage medium |
| TWI844899B (en) * | 2021-08-20 | 2024-06-11 | 聯發科技股份有限公司 | A wireless device and a method for determining a location of the wireless device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645222A (en) * | 2012-04-10 | 2012-08-22 | 惠州市德赛西威汽车电子有限公司 | Satellite inertial navigation method and equipment |
| EP2555017A1 (en) * | 2011-08-03 | 2013-02-06 | Harman Becker Automotive Systems GmbH | Vehicle navigation on the basis of satellite positioning data and vehicle sensor data |
| US20140361927A1 (en) * | 2013-06-07 | 2014-12-11 | Cambridge Sillicon Radio Limited | Terrestrial positioning assistance serving based on receiver device context |
| CN106569241A (en) * | 2016-09-27 | 2017-04-19 | 北京航空航天大学 | Single frequency high precision positioning method based on GNSS |
-
2019
- 2019-03-07 CN CN201910172162.6A patent/CN111665533A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2555017A1 (en) * | 2011-08-03 | 2013-02-06 | Harman Becker Automotive Systems GmbH | Vehicle navigation on the basis of satellite positioning data and vehicle sensor data |
| CN102645222A (en) * | 2012-04-10 | 2012-08-22 | 惠州市德赛西威汽车电子有限公司 | Satellite inertial navigation method and equipment |
| US20140361927A1 (en) * | 2013-06-07 | 2014-12-11 | Cambridge Sillicon Radio Limited | Terrestrial positioning assistance serving based on receiver device context |
| CN106569241A (en) * | 2016-09-27 | 2017-04-19 | 北京航空航天大学 | Single frequency high precision positioning method based on GNSS |
Non-Patent Citations (1)
| Title |
|---|
| 耿华;张涛;连小珉;杨殿阁;: "北斗卫星与车载传感器融合的车辆定位" * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112416015A (en) * | 2020-11-11 | 2021-02-26 | 国网安徽省电力有限公司淮南供电公司 | Power tower inspection unmanned aerial vehicle anti-lost control method and system |
| TWI844899B (en) * | 2021-08-20 | 2024-06-11 | 聯發科技股份有限公司 | A wireless device and a method for determining a location of the wireless device |
| CN115469344A (en) * | 2022-06-10 | 2022-12-13 | 北京罗克维尔斯科技有限公司 | Vehicle positioning method and device, vehicle, electronic equipment and storage medium |
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