CN116299285A - MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing - Google Patents
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Abstract
The invention relates to a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, which utilizes a double-base centralized MIMO radar to realize the transmission and the reception of signals, wherein the transmission signals are OFDM radar communication integrated signals. In order to better utilize the correlation of signals among array elements, a distributed compressed sensing technology is adopted to process radar communication integrated echo signals, the echo signals are sampled at a sampling rate far lower than the Nyquist law, when the signals are sparse or compressible, the original signals can be accurately or approximately reconstructed by using a small amount of sampling signals, and the computational complexity can be greatly reduced.
Description
Technical Field
The invention belongs to the technical field of radar communication, and particularly relates to a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing.
Background
Along with the development of information technology, the systems such as a multifunctional radar, an intelligent automobile, a 5G antenna and the like are more and more complex, and the requirements of the systems on a radar detection function and a wireless communication function are more and more increased, so that the radar and the communication equipment are widely applied in military and civil aspects. To achieve the functions of both devices, the conventional approach is to simply put the radar and communication devices together independently, each functioning. However, due to lack of unified scheduling and management, the manner of the subsystem often restricts the performance of the whole system due to the problems of energy consumption, volume, spectrum interference and the like.
The prior art mainly aims at realizing target positioning of the traditional radar signal, and does not consider combining two functions of radar and communication into one signal waveform to realize the functions of target positioning and data communication at the same time. And in the prior art, conventional signal processing requires sampling at twice the highest frequency of the original signal according to Nyquist (Nyquist) sampling law, whereas in actual engineering operation, this multiple is higher, typically up to four or five times. Therefore, when a large bandwidth signal such as millimeter wave is faced, a large amount of sampling data needs to be acquired in order to restore the original signal, which also causes problems. On the one hand, for the conventional signal processing method, complete data sampling is unnecessary, so that a great amount of redundant information exists in the received echo data; and the high-speed sampling receiver is also relatively expensive, which causes a certain waste of resources of the hardware system. On the other hand, high-rate sampling directly results in increased sampled data, which presents great challenges for subsequent transmission, storage, and the like.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing. The technical problems to be solved by the invention are realized by the following technical scheme:
the invention provides a distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method, which utilizes a bistatic centralized MIMO radar to realize the transmission and the reception of signals, wherein the transmission signals are OFDM radar communication integrated signals, and the waveform of the OFDM radar communication integrated signals contains N p Pulses each consisting of N s The OFDM symbol is composed of N subcarriers c The subcarrier spacing is deltaf, the signal length of one OFDM is T s Pulse repetition period is T p ；
The method comprises the following steps:
acquiring a receiving signal of a bistatic centralized MIMO radar;
constructing a joint sparse positioning model, sparsely representing the received signals, selecting a measurement matrix, and compressing the sparse signals to obtain compressed signals;
and reconstructing the compressed signal by adopting a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal, and realizing target positioning.
In one embodiment of the present invention, the OFDM radar communication integration signal is expressed as:
wherein d p,s,n Representing the communication information carried by the nth subcarrier in the nth pulse, c representing the speed of light,
representing rectangular window functionsWhen T is more than or equal to 0 and less than or equal to T s When it takes a value of 1, otherwise takes a value of 0, the total bandwidth of the signal b=n c Δf。
In one embodiment of the invention, the bistatic centralized MIMO radar comprises N T Multiple transmit antennas and N R A plurality of receiving antennas with a spacing d respectively T And d R And (2) and
the received signal of the bistatic centralized MIMO radar is expressed as:
wherein K represents the number of targets, σ k Representing the reflection coefficient of the kth target,
a reception steering vector representing a kth object, < >>
Represents the angle of arrival, a (θ k ) Emission steering vector, θ, for the kth target k The departure angle of the kth target is represented, S represents the OFDM radar communication integrated signal received by all receiving antennas, and W is additive Gaussian white noise, ( T Representing a transpose;
the received signal of the nth receiving antenna of the bistatic centralized MIMO radar is expressed as:
wherein w is n And s represents the OFDM radar communication integrated signal received by the nth receiving antenna.
In one embodiment of the present invention, constructing a joint sparse positioning model, sparsely representing the received signal, selecting a measurement matrix, and compressing the sparse signal to obtain a compressed signal, including:
dividing the plane where the MIMO radar is located into L 1 ×L 2 The angular positions are expressed as:
wherein θ represents the angle of departure,
representing the angle of arrival;
defining a reflection coefficient and a sparse basis in the plane, wherein the reflection coefficient is as follows:
taking a position set formed by all angles of the plane division as a selected angle domain sparse basis, and under the angle domain sparse model, the received signal of the nth receiving antenna is expressed as:
the sparse basis of the nth receiving antenna in the joint positioning sparse model is as follows:
and according to the sparse basis and the reflection coefficient, representing the received signal of the nth receiving antenna as:
r n ＝(Ψ n σ n ) T ；
σ n ＝[σ 11 σ 12 … σ l1l2 … σ L1L2 ]；
in sigma n Representing the received signal r n At sparse base ψ n The sparse vector below;
selecting a Gaussian random matrix as a measurement matrix, and projecting the received signal of each receiving antenna on the measurement matrix to obtain a corresponding compressed signal, wherein the compressed signal corresponding to the received signal of the nth receiving antenna is expressed as:
in phi, phi n Representing the measurement matrix, Θ n Representing the perception matrix, Θ n ＝Φ n Ψ n 。
In one embodiment of the present invention, a joint sparse reconstruction algorithm is adopted to reconstruct the compressed signal to obtain a target position estimation result and a sparse vector estimation result of the signal, so as to realize target positioning, including:
performing loop iteration on the compressed signals corresponding to each receiving antenna by adopting a joint sparse reconstruction algorithm to obtain a corresponding target position estimation result and a sparse vector estimation result;
and averaging the target position estimation results and the sparse vector estimation results corresponding to all the receiving antennas to obtain the target position estimation results and the sparse vector estimation results of the signals, thereby realizing target positioning.
In one embodiment of the invention, for the compressed signal y corresponding to the nth receive antenna n The cyclic iteration process of the joint sparse reconstruction algorithm comprises the following steps:
step a: initializing parameters of a joint sparse reconstruction algorithm to enable
t=1, wherein r n,t Representing the compressed signal y n Difference after the t-th iteration, +.>
Representation ofEmpty set, P n,t Representing the column index determined after each iteration, Ω n,t Representing the compressed signal y n An index set after t iterations, wherein t represents the iteration times;
step b: the perception matrix Θ is calculated according to the following formula n And the difference r n,t Is a correlation coefficient vector of (a):
wherein < · > represents the product of the vector, and |·| represents the absolute value;
step c: selecting a correlation coefficient vector u t Column index P is added to column number P corresponding to the maximum value in the table n,t In (2) updating omega n,t And theta (theta) n,t The method comprises the following steps:
Ω n,t ＝Ω n,t-1 ∪P n,t ，Θ n,t ＝Θ n,t-1 ∪γ p ；
wherein Θ is n,t The representation is according to index Ω n,t From the perception matrix Θ n Selected column set of (a), gamma p Representing the perception matrix Θ n,t P-th column of (2);
step d: y is calculated as follows n ＝Θ n,t σ n,t The least squares solution of (2) is:
step e: from the solution sigma n,t Calculating new measured signal values
Updating the difference r based on the new measured signal value n,t Is->
Step f: let t=t+1, if t<K returning to the step b to continue the laminationIf t is more than or equal to K, stopping iteration, and gathering index omega after iteration is completed n,t As the compressed signal y n Target position estimation result omega of (2) n Calculating sigma in the iterative process n,t Storing the compressed signals y into a set to obtain the compressed signals y n Is a sparse vector estimation result of (2)
Where K represents the sparseness of the signal.
Compared with the prior art, the invention has the beneficial effects that:
according to the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, a new OFDM radar communication integrated signal is designed by combining with the current OFDM communication frame structure, so that the data rate of a transmitted signal is improved, the designed signal is more similar to the communication frame structure, and therefore synchronization is easier to realize. And the reconstruction algorithm based on distributed compressed sensing is considered for the bistatic MIMO-OFDM radar communication integrated system, so that the data volume required by echo sampling can be greatly reduced, and the position information of the target can be accurately obtained.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention, as well as the preferred embodiments thereof, together with the following detailed description of the invention, given by way of illustration only, together with the accompanying drawings.
Drawings
Fig. 1 is a schematic diagram of a conventional OFDM radar signal provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of an OFDM radar communication integrated signal provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of target positioning according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a method for positioning an integrated target of MIMO-OFDM radar communication based on distributed compressed sensing according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an actual target location provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of an estimated target location provided by an embodiment of the present invention;
FIG. 7 is a reconstruction probability of an algorithm provided by an embodiment of the present invention;
FIG. 8 is a graph of the relative positioning error of a target with the signal-to-noise ratio for the distributed compressed sensing and the conventional compressed sensing method according to the embodiment of the present invention;
fig. 9 is a graph of a communication error rate of an OFDM radar communication integrated signal according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the invention provides a MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, which is described in detail below with reference to the accompanying drawings and the specific embodiments.
The foregoing and other features, aspects, and advantages of the present invention will become more apparent from the following detailed description of the preferred embodiments when taken in conjunction with the accompanying drawings. The technical means and effects adopted by the present invention to achieve the intended purpose can be more deeply and specifically understood through the description of the specific embodiments, however, the attached drawings are provided for reference and description only, and are not intended to limit the technical scheme of the present invention.
Example 1
According to the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, the transmission and the reception of signals are achieved through the double-base centralized MIMO radar, wherein the transmission signals are OFDM radar communication integrated signals.
Orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM) signals are widely used by virtue of the advantages of suitability for high-speed data transmission, high spectrum utilization rate, strong fading resistance and the like, but in the traditional mode, the OFDM signals have larger difference in communication and radar application, and the waveform designs are different, so that the design of integrated signals is required on the basis of meeting the requirements of millimeter wave radar communication integrated systems.
Referring to fig. 1, fig. 1 is a schematic diagram of a conventional OFDM radar signal according to an embodiment of the present invention, where each pulse of the conventional OFDM radar signal transmits only one OFDM symbol. In this case, the data rate of the transmitted signal is limited to a certain extent, and the data rate is greatly different from the communication frame structure, so that the data rate cannot be interconnected and interworked with the active data link network.
Therefore, the invention designs an OFDM radar communication integrated signal, as shown in fig. 2, which is a schematic diagram of the OFDM radar communication integrated signal provided by the embodiment of the invention, and the OFDM radar communication integrated signal adopts a pulse system waveform, and each pulse of the waveform is formed by a plurality of OFDM signals, so that the data transmission rate of the signal can be improved under the same bandwidth. And the pulse formed by a plurality of OFDM signals can be regarded as one time slot in communication, which is more similar to the communication signals, so that the communication can be completed in one pulse, and the synchronization is easier to realize than the traditional OFDM radar waveform.
In the present embodiment, the waveform of the OFDM radar communication integrated signal includes N p Pulses each consisting of N s The OFDM symbol is composed of N subcarriers c The subcarrier spacing is deltaf, the signal length of one OFDM is T s Pulse repetition period is T p Then, the OFDM radar communication integration signal is expressed as:
wherein d p,s,n Representing the communication information carried by the nth subcarrier in the nth pulse, c representing the speed of light,
represents a rectangular window function when T is more than or equal to 0 and less than or equal to T s When it takes a value of 1, otherwise takes a value of 0, the total bandwidth of the signal b=n c Δf。
The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing in the embodiment adopts the distributed compressed sensing method to realize the positioning of the target. First, a distributed compressed sensing method will be described.
The distributed compressed sensing (Distributed Compressed Sensing, DCS) method is to build a joint sparse model (Joint Sparse Model, JSM) and utilize correlation between signals to realize joint compression and reconstruction of multiple signals, and compared with the traditional compressed sensing method, the method can save a certain number of measurement points and improve measurement accuracy. The DCS method is based on joint sparsity of signal groups, and in the joint sparse model adopted by the invention, no public part exists in the signal groups, all signals can be represented by the same sparse basis, and the sparsity of each signal is the same and only has different coefficients. The signals in the signal group can be expressed as:
x j ＝Ψs j ,j∈{1,2,…,J} (2)；
if x j Is K, the set omega is used for representing the sparse vector s j Index positions of non-zero elements in the list are
||Ω|| 0 =k. Omega is the same for all signals in this joint sparse model, and is therefore also referred to as the sparse structure of the signal group.
One practical situation of the model simulation is that the antenna array receives the signal reflected by the target at the same time, and the signal has different phase shifts and channel fading in space propagation due to multipath effect, but is sparse on sparse bases such as frequency domain, angle domain and the like.
Further, for a description of the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing in this embodiment, please refer to the schematic diagram of the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing shown in fig. 4, the method includes:
step 1: acquiring a receiving signal of a bistatic centralized MIMO radar;
in the present embodimentIn an example, a bistatic centralized MIMO radar includes N T Multiple transmit antennas and N R A plurality of receiving antennas with a spacing d respectively T And d R And (2) and
assuming K targets in the far field, the azimuth of the kth target is
k=1, 2, …, K. Wherein θ is DOD,)>
For DOA, in the transmitting and receiving antenna arrays, the first antenna is taken as a reference unit, and then:
the emission steering vector for the kth target is:
the reception steering vector of the kth target is:
then, the received signal of the bistatic centralized MIMO radar is expressed as:
wherein K represents the number of targets, σ k Representing the reflection coefficient of the kth target,
a reception steering vector representing a kth object, < >>
Representing the kth targetAngle of arrival, S (θ) k ) Emission steering vector, θ, for the kth target k The departure angle of the kth target is represented, S represents the OFDM radar communication integrated signal received by all receiving antennas, and W is additive Gaussian white noise, ( T Representing the transpose.
The received signal of the nth receiving antenna of the bistatic localized MIMO radar is expressed as:
wherein w is n And s represents the OFDM radar communication integrated signal received by the nth receiving antenna.
In the centralized MIMO-OFDM radar system, the data received by each receiving antenna is echo data after being reflected by the same target, and if the angle space of the plane where the transmitting antenna and the receiving antenna are located is regarded as a sparse basis, the received signals satisfy the joint sparse model.
Step 2: constructing a joint sparse positioning model, performing sparse representation on the received signals, and selecting a measurement matrix to perform compression processing on the sparse signals to obtain compressed signals;
in this embodiment, step 2 includes:
step 2.1: constructing a joint sparse positioning model, and performing sparse representation on a received signal;
in the present embodiment, a two-dimensional plane formed by the departure angle (Direction of Departure, DOD) and the arrival angle (Direction of Arrival, DOA) is regarded as a grid structure, namely, the divided plane is regarded as being formed by a plurality of DODs and DOAs, and as shown in FIG. 3, the plane in which the MIMO radar is located is divided into L 1 ×L 2 The angular positions are expressed as:
wherein θ represents the angle of departure,
representing the angle of arrival.
Step 2.2: defining a reflection coefficient and a sparse basis in the plane, wherein the reflection coefficient is as follows:
assuming that the object is an ideal point object in space, since the angles divided by each lattice are known, the position of the object in the two-dimensional plane can be represented by a matrix (a matrix element of 0 indicates no object, a reflection coefficient
The matrix element other than 0 indicates that there is a target present, reflection coefficient +.>
Taking a position set formed by all angles of the plane division as a selected angle domain sparse basis, and under the angle domain sparse model, the received signal of the nth receiving antenna is expressed as:
the sparse basis of the nth receiving antenna in the joint positioning sparse model is as follows:
step 2.3: based on the sparse basis and the reflection coefficient, the received signal of the nth receiving antenna is expressed as:
r n ＝(Ψ n σ n ) T (11)；
in sigma n Representing the received signal r n At sparse base ψ n Sparse vectors below.
In the present embodiment, σ n Non-zero element ρ in k Is the angular position at which the target is located.
It should be noted that, because the waveform of the transmitted OFDM radar communication integrated signal is millimeter wave, the wavelength is smaller, and the array pitch is generally half-wavelength, which will make the pitch of each array element smaller in the centralized MIMO radar, so the reflection coefficient of the same target in each array element can be regarded as the same.
Step 2.4: selecting a Gaussian random matrix as a measurement matrix, and projecting the received signal of each receiving antenna on the measurement matrix to obtain a corresponding compressed signal, wherein the compressed signal corresponding to the received signal of the nth receiving antenna is expressed as:
in phi, phi n Representing the measurement matrix, Θ n Representing the perception matrix, Θ n ＝Φ n Ψ n 。
Step 3: and reconstructing the compressed signal by adopting a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal, and realizing target positioning.
In an alternative embodiment, step 3 comprises:
step 3.1: performing loop iteration on the compressed signals corresponding to each receiving antenna by adopting a joint sparse reconstruction algorithm to obtain a corresponding target position estimation result and a sparse vector estimation result;
in the present embodiment, for the compressed signal y corresponding to the nth receiving antenna n Inputs to the joint sparse reconstruction algorithm include: m x N-dimensional sense matrix Θ n M x 1-dimensional compressed signal y n And sparsity K of the signal. The output result comprises: target position estimation result omega of signal n And sparse vector estimation results
The specific loop iteration process comprises the following steps:
step a: initializing parameters of a joint sparse reconstruction algorithm to enable
t=1, wherein r n,t Representing the compressed signal y n Difference after the t-th iteration, +.>
Representing empty set, P n,t Representing the column index determined after each iteration, Ω n,t Representing the compressed signal y n An index set after t iterations, wherein t represents the iteration times;
step b: the perception matrix Θ is calculated according to the following formula n And the difference r n,t Is a correlation coefficient vector of (a):
wherein < · > represents the product of the vector, and |·| represents the absolute value;
step c: selecting a correlation coefficient vector u t Column index P is added to column number P corresponding to the maximum value in the table n,t In (2) updating omega n,t And theta (theta) n,t The method comprises the following steps:
Ω n,t ＝Ω n,t-1 ∪P n,t ，Θ n,t ＝Θ n,t-1 ∪γ p (15)；
wherein Θ is n,t The representation is according to index Ω n,t From the perception matrix Θ n Selected column set of (a), gamma p Representing the perception matrix Θ n P-th column of (2);
in step c, the correlation coefficient vector u t Columns corresponding to the maximum value in (i.e. table)Indicating perception matrix theta n Intermediate and difference r n,t The column with the strongest correlation will be the vector u of the correlation numbers t The position P of the maximum value is stored in P n,t In the container, P is repeated once per iteration n,t Will increase one number and will find P n,t Logging omega n,t Is a kind of medium. At the same time, the matrix theta will be perceived n The column with the strongest correlation is stored into Θ n,t 。
In the present embodiment, Ω is designed n,t Length of L 1 *L 2 ，Ω n,t The initial values are all 0, P n,t Is a number corresponding to a position on a grid and is stored in omega n,t After that, omega can be obtained n,t The 0 of the corresponding position becomes 1.
Step d: y is calculated as follows n ＝Θ n,t σ n,t The least squares solution of (2) is:
step e: from the solution sigma n,t Calculating new measured signal values
Updating the difference r based on the new measured signal value n,t Is->
Step f: let t=t+1, if t<K, returning to the step b to continue iteration, if t is more than or equal to K, stopping iteration, and collecting the index set omega after the iteration is completed n,t As the compressed signal y n Target position estimation result omega of (2) n Calculating sigma in the iterative process n,t Storing the compressed signals y into a set to obtain the compressed signals y n Is a sparse vector estimation result of (2)
Where K represents the sparseness of the signal.
In this embodiment, when the algorithm is run K times, the algorithm loop of one receiving antenna is ended, and K pieces of position information, namely Ω, are obtained n Among them, K are 1, and the rest are 0. The calculated sigma at these K positions n,t The corresponding values are stored in the set to obtain a sparse vector estimation result
Step 3.2: and averaging the target position estimation results and the sparse vector estimation results corresponding to all the receiving antennas to obtain the target position estimation results and the sparse vector estimation results of the signals, thereby realizing target positioning.
In this embodiment, by averaging the target position estimation results and the sparse vector estimation results corresponding to all the receiving antennas, the estimated target position can be made more accurate.
According to the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing, a new OFDM radar communication integrated signal is designed by combining with the current OFDM communication frame structure, so that the data rate of a transmitted signal is improved, the designed signal is more similar to the communication frame structure, and therefore synchronization is easier to realize. And the reconstruction algorithm based on distributed compressed sensing is considered for the bistatic MIMO-OFDM radar communication integrated system, so that the data volume required by echo sampling can be greatly reduced, and the position information of the target can be accurately obtained.
Example two
The effect of the MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing according to the first embodiment is described through a simulation experiment. The simulation parameters are shown in table 1.
TABLE 1 simulation parameters for MIMO-OFDM radar communication integration system
Referring to fig. 5 and 6, fig. 5 is a schematic view of an actual target position according to an embodiment of the present invention; FIG. 6 is a schematic diagram of an estimated target location provided by an embodiment of the present invention. The actual coordinate position of the target can also be calculated by MIMO transmitting and receiving the positions of the radar stations. Two graphs show that the distributed compressed sensing method is adopted to sample the echo signals of the MIMO-OFDM radar communication integration, and the actual position of the target can be acquired more accurately. In addition, in the simulation process, only a small number of sampling points are adopted, and compared with the traditional Nyquist sampling data, the data volume is greatly reduced, and the calculation complexity can be obviously reduced.
Referring to the reconstruction probability of the algorithm provided by the embodiment of the present invention shown in fig. 7, it can be shown from the graph that when the sparseness of the signal is fixed, the reconstruction probability increases with the increase of the number of measurement samples; when the reconstruction probability is fixed, the number of measurement samples required for recovering the signal with larger sparsity to the corresponding reconstruction probability is also increased, and all the signals accord with theoretical expectation.
Referring to fig. 8, in order to further reduce the target positioning reconstruction time, i.e. after enlarging the DOA grid spacing, the distributed compressed sensing and traditional compressed sensing methods provide a graph of the target relative positioning error with the change of signal to noise ratio, in the simulation process of the graph, 100 monte carlo experiments are adopted under each signal to noise ratio, and then the average value of the relative positioning errors is calculated. Simulation shows that the traditional compressed sensing method has poor robustness under the condition, and the situation that the estimated value deviates from the actual value greatly often occurs. The algorithm based on distributed compressed sensing fully utilizes the correlation between signals and in the signals, the reconstructed target position has better robustness, and more accurate target position information can be obtained in practical application.
Please refer to fig. 9, which is a graph of a communication error rate of an OFDM radar communication integrated signal according to an embodiment of the present invention. As the signal-to-noise ratio increases, the communication error rate of the OFDM integrated signal decreases; and along with the increase of the signal modulation order, the communication error rate is increased along with the increase of the signal modulation order, and the communication error rate accords with theoretical expectation.
It should be noted that in this document relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in an article or apparatus that comprises the element.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (6)
1. The MIMO-OFDM radar communication integrated target positioning method based on distributed compressed sensing is characterized in that a bistatic centralized MIMO radar is utilized to realize the transmission and the reception of signals, wherein the transmission signals are OFDM radar communication integrated signals, and the waveform of the OFDM radar communication integrated signals contains N p Pulses each consisting of N s The OFDM symbol is composed of N subcarriers c The subcarrier spacing is deltaf, the signal length of one OFDM is T s Pulse repetition period is T p ；
The method comprises the following steps:
acquiring a receiving signal of a bistatic centralized MIMO radar;
constructing a joint sparse positioning model, sparsely representing the received signals, selecting a measurement matrix, and compressing the sparse signals to obtain compressed signals;
and reconstructing the compressed signal by adopting a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal, and realizing target positioning.
2. The distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method according to claim 1, wherein the OFDM radar communication integrated signal is expressed as:
wherein d p,s,n Representing the communication information carried by the nth subcarrier in the nth pulse, c representing the speed of light,
represents a rectangular window function when T is more than or equal to 0 and less than or equal to T s When it takes a value of 1, otherwise takes a value of 0, the total bandwidth of the signal b=n c Δf。
3. The distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method of claim 1, wherein the bistatic centralized MIMO radar comprises N T Multiple transmit antennas and N R A plurality of receiving antennas with a spacing d respectively T And d R And (2) and
the received signal of the bistatic centralized MIMO radar is expressed as:
wherein K represents the number of targets, σ k Representing the reflection coefficient of the kth target,
a reception steering vector representing a kth object, < >>
Represents the angle of arrival, a (θ k ) Emission steering vector, θ, for the kth target k The departure angle of the kth target is represented, S represents the OFDM radar communication integrated signal received by all receiving antennas, and W is additive Gaussian white noise, ( T Representing a transpose;
the received signal of the nth receiving antenna of the bistatic centralized MIMO radar is expressed as:
wherein w is n And s represents the OFDM radar communication integrated signal received by the nth receiving antenna.
4. The distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method of claim 3, wherein constructing a joint sparse positioning model, sparsely representing the received signal, selecting a measurement matrix, and compressing the sparse signal to obtain a compressed signal, comprises:
dividing the plane where the MIMO radar is located into L 1 ×L 2 The angular positions are expressed as:
wherein θ represents the angle of departure,
representing the angle of arrival;
defining a reflection coefficient and a sparse basis in the plane, wherein the reflection coefficient is as follows:
taking a position set formed by all angles of the plane division as a selected angle domain sparse basis, and under the angle domain sparse model, the received signal of the nth receiving antenna is expressed as:
the sparse basis of the nth receiving antenna in the joint positioning sparse model is as follows:
and according to the sparse basis and the reflection coefficient, representing the received signal of the nth receiving antenna as:
r n ＝(Ψ n σ n ) T ；
in sigma n Representing the received signal r n At sparse base ψ n The sparse vector below;
selecting a Gaussian random matrix as a measurement matrix, and projecting the received signal of each receiving antenna on the measurement matrix to obtain a corresponding compressed signal, wherein the compressed signal corresponding to the received signal of the nth receiving antenna is expressed as:
in phi, phi n Representing the measurement matrix, Θ n Representing the perception matrix, Θ n ＝Φ n Ψ n 。
5. The distributed compressed sensing-based MIMO-OFDM radar communication integrated target positioning method of claim 4, wherein reconstructing the compressed signal by using a joint sparse reconstruction algorithm to obtain a target position estimation result and a sparse vector estimation result of the signal, and implementing target positioning, comprises:
performing loop iteration on the compressed signals corresponding to each receiving antenna by adopting a joint sparse reconstruction algorithm to obtain a corresponding target position estimation result and a sparse vector estimation result;
and averaging the target position estimation results and the sparse vector estimation results corresponding to all the receiving antennas to obtain the target position estimation results and the sparse vector estimation results of the signals, thereby realizing target positioning.
6. The method for integrated target positioning for MIMO-OFDM radar communication based on distributed compressed sensing as claimed in claim 5, wherein for the compressed signal y corresponding to the nth receiving antenna n The cyclic iteration process of the joint sparse reconstruction algorithm comprises the following steps:
step a: initializing parameters of a joint sparse reconstruction algorithm to let r n,t ＝y n ，
t=1, wherein r n,t Representing the compressed signal y n Difference after the t-th iteration, +.>
Representing empty set, P n,t Representing the column index determined after each iteration, Ω n,t Representing the compressed signal y n An index set after t iterations, wherein t represents the iteration times;
step b: the perception matrix Θ is calculated according to the following formula n And the difference r n,t Is a correlation coefficient vector of (a):
wherein < · > represents the product of the vector, and |·| represents the absolute value;
step c: selecting a correlation coefficient vector u t Column index P is added to column number P corresponding to the maximum value in the table n,t In (2) updating omega n,t And theta (theta) n,t The method comprises the following steps:
Ω n,t ＝Ω n,t-1 ∪P n,t ，Θ n,t ＝Θ n,t-1 ∪γ p ；
wherein Θ is n,t The representation is according to index Ω n,t From the perception matrix Θ n Selected column set of (a), gamma p Representing the perception matrix Θ n,t P-th column of (2);
step d: y is calculated as follows n ＝Θ n,t σ n,t The least squares solution of (2) is:
step e: from the solution sigma nt Calculating new measured signal values
Updating the difference r based on the new measured signal value n,t Is->
Step f: let t=t+1, ift<K, returning to the step b to continue iteration, if t is more than or equal to K, stopping iteration, and collecting the index set omega after the iteration is completed n,t As the compressed signal y n Target position estimation result omega of (2) n Calculating sigma in the iterative process n,t Storing the compressed signals y into a set to obtain the compressed signals y n Is a sparse vector estimation result of (2)
Where K represents the sparseness of the signal.
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