JP2024131897A - Attitude and orientation estimation device, satellite communication earth station, and attitude and orientation estimation method - Google Patents

Abstract

[Problem] To improve an attitude angle estimation error. [Solution] An attitude and orientation estimation device includes an arrival angle measurement unit, an inertial measurement unit, an absolute coordinate output unit, and an attitude and orientation calculation unit. The arrival angle measurement unit receives radio waves transmitted from a satellite and measures the arrival angle of the received radio waves. The inertial measurement unit includes at least an accelerometer and an angular velocity meter. The absolute coordinate output unit acquires absolute coordinates of the current location. The attitude and orientation calculation unit calculates a gravity direction measurement value by subtracting a linear acceleration estimated in advance from the acceleration measured by the accelerometer, generates a first difference vector which is the difference from the gravity direction estimated from the estimated attitude in advance, generates a second difference vector which is the difference between the measured arrival angle acquired by the arrival angle measurement unit and the arrival angle estimated from the absolute coordinates, orbit information of the satellite, and the estimated attitude in advance, and updates the estimated attitude in advance using a complementary filter based on the first difference vector and the second difference vector to acquire an estimated attitude. [Selected Figure] Figure 1

Description

Embodiments of the present invention relate to an attitude and orientation estimation device, a satellite communication earth station, and an attitude and orientation estimation method.

As long as there is line of sight to the satellite, satellite communications can be used even in environments where normal communication lines, optical cables, mobile phones, and Wi-Fi cannot be used. There is particularly high demand for satellite communications attached to cars or ships for communication while moving. When the satellite being communicated with is a geostationary satellite, the angle between geostationary satellites is small, at just a few degrees, so the beam must be aimed with high precision to prevent radio waves from leaking into other satellites. When an earth station is attached to a moving object, the beam must be kept aimed at the satellite with high precision while moving.

Linear polarization is often used in satellite communications using geostationary satellites. Polarization has two orthogonal axes, and different data is assigned to each. With linear polarization, even a slight deviation in the polarization angle can leak into the cross polarization, causing interference waves and lowering the communication quality of lines communicating with cross polarization. Therefore, the cross polarization ratio during transmission is specified, and is a very strict value, for example, 30 dB or more (within ±1.8 degrees of the polarization angle of linear polarization). The direction and attitude of a moving earth station change as it moves. In the case of linear polarization, even if the antenna outputs a constant polarization, if the direction or attitude of the antenna changes, the polarization seen by the satellite will change, and there is a possibility that the standard will not be met. For this reason, mobile earth stations are equipped with an IMU (Inertial Measurement Unit) that includes sensors such as a gyroscope and accelerometer, and they need to estimate their own attitude from the sensor output and control it so that the polarization seen by the satellite is always within the standard.

The AHRS (Attitude Heading Reference System) is a method for estimating heading and attitude. There are many AHRS methods, but when high-level attitude control is required as described above, the algorithms that can be used are limited. One of these is a complementary filter that uses a Kalman filter, which estimates the attitude angle, gyro bias, linear acceleration, and magnetic disturbance while repeatedly processing with the filter.

“Freescale Sensor Fusion Kalman Filter (fusion.c) Technical Note,” https://github.com/memsindustrygroup/Open-Source-Sensor-Fusion/tree/master/docs [Internet], accessed March 10, 2023 MATLAB (registered trademark) Sensor fusion and tracking toolbox, imufilter, https://jp.mathworks.com/help/fusion/ref/imufilter-system-object.html [Internet], confirmed on March 10, 2023 D. Roetenberg, et. al., "Compensation of Magnetic Disturbances Improves Inertial and Magnetic Sensing of Human Body Segment Orientation", IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 13, No. 3, September 2004

The above technologies include a method that does not use magnetism to suppress the significant deterioration in accuracy caused by the effects of magnetic disturbances due to metal, and instead integrates raw sensor output data without estimating orientation to determine only attitude. This is a filter that estimates three conditions - attitude angle error, gyro bias error, and linear acceleration error - from a single measured value, the difference in gravity vectors. How one system of error is allocated to the three is determined by the various covariance values used in the Kalman filter, but because the conditions change dynamically, it is difficult to allocate the errors correctly, and as a result, when there are drastic changes in movement, not only the orientation accuracy but also the attitude accuracy is worse than with algorithms that use magnetism.

The embodiment of the present invention solves such problems, and as a non-limiting example of the problem, provides an attitude and orientation estimation device that improves the attitude angle estimation error.

According to one embodiment, the attitude and orientation estimation device includes an angle of arrival measurement unit, an inertial measurement unit, an absolute coordinate output unit, and an attitude and orientation calculation unit. The angle of arrival measurement unit receives radio waves transmitted from a satellite and measures the angle of arrival of the received radio waves. The inertial measurement unit includes at least an accelerometer and an angular velocity meter. The absolute coordinate output unit acquires absolute coordinates of the current location. The attitude and orientation calculation unit calculates a gravity direction measurement value by subtracting a linear acceleration estimated in advance from the acceleration measured by the accelerometer, generates a first difference vector which is the difference from the gravity direction estimated from the estimated attitude in advance, generates a second difference vector which is the difference between the measured angle of arrival acquired by the angle of arrival measurement unit and the angle of arrival estimated from the absolute coordinates, the satellite orbit information, and the estimated attitude in advance, and updates the estimated attitude in advance using a complementary filter based on the first difference vector and the second difference vector to acquire an estimated attitude.

FIG. 1 is a diagram illustrating an example of an attitude and orientation estimation device according to an embodiment. 4 is a graph showing an example of an output of an attitude and orientation estimation device according to an embodiment. FIG. 1 is a diagram illustrating an example of an attitude and orientation estimation device according to an embodiment. FIG. 2 is a diagram illustrating an example of a satellite communication earth station according to an embodiment. FIG. 1 is a diagram showing an example of the relationship between polarization and attitude. 4 is a flowchart showing an example of a process of an attitude and orientation estimation device according to an embodiment.

The following describes the embodiment with reference to the drawings. In the following description, only the parts that are essential to the configuration of the embodiment are shown, and illustrations and explanations of parts that are not related to the operation of the embodiment may be omitted.

In a Kalman filter, the parameters estimated within the filter loop are called "states", and estimation proceeds in two stages: prediction and update. The states estimated by this algorithm are not the actual targets of the algorithm, such as attitude angle and gyro bias, but error parameters such as attitude angle error, gyro bias error, linear acceleration error, and magnetic disturbance error. Because they are errors, the state prediction value is set to 0 in the prediction step.

The "measurement value" of the Kalman filter, i.e., the measurement data used to calculate the state "update" value by subtracting the predicted value from it and multiplying it by the Kalman gain, is the gravity vector due to the acceleration measured by the accelerometer. In the conventional method, the magnetic vector measured by the magnetic sensor is also used. Specifically, the "measurement value" is made up of two values: (1) the gravity vector calculated by subtracting the predicted linear acceleration from the measured acceleration, and the difference vector obtained by subtracting the gravity vector corresponding to the predicted downward direction from the predicted attitude, and (2) the difference vector between the north direction vector calculated by subtracting the predicted magnetic disturbance from the measured magnetism, and the north direction vector predicted from the predicted attitude direction. As mentioned above, the state prediction value is 0, so the state estimate is calculated by directly multiplying this measurement value by the Kalman gain. Note that this filter is a complementary filter that uses the error vector as the measurement value.

The updated error state is used to update the values of attitude angle, gyro bias, linear acceleration, and magnetic disturbance. The predicted values for the next step are calculated as follows: the attitude angle predicted value is from the gyro measurement data, the gyro bias predicted value is the estimated value from the previous step as is, and the linear acceleration and magnetic disturbance predicted values are calculated by multiplying the estimated values from the previous step by a coefficient between 0 and 1, which was previously a fixed value. With a normal Kalman filter, a "state" predicted value is created from the updated value of the previous step to form a loop, but with this filter, the predicted value of the "state" is 0, so multiple vectors that form the "measured values" are generated from the predicted values of the attitude angle, etc. to form a loop.

Although it uses the geomagnetic field measured by a magnetic sensor, the magnetic vector measured by the magnetic sensor often does not indicate the correct north. There are two main reasons for this. One is that magnetic fields emitted by nearby magnetized iron products and the other is that the direction indicated by the geomagnetic field is not the correct north. If there is magnetized metal nearby, it is not uncommon for the direction to be indicated to be far removed from north. Therefore, if you want to calculate the attitude angle with high accuracy to obtain a cross polarization ratio of 30 dB, an algorithm that uses magnetism is not suitable.

There are also algorithms that do not use magnetism and disregard the accuracy of the heading to find only the attitude. There is a method that removes the magnetic vector from the "measurement value" and estimates the attitude angle error, gyro bias error, and linear acceleration error from only the difference in the gravity vector.

(First embodiment)
1 shows a typical implementation according to an embodiment. The attitude and orientation estimation device 100 includes an angle-of-arrival measurement unit 2, an inertial measurement unit (hereinafter referred to as an IMU 3), an absolute coordinate output unit 4, an antenna 5, and a received radio wave processing unit 6.

The arrival angle measurement unit 2 performs RF and baseband processing such as down-conversion and filtering on the radio waves received by the antenna 5 in the received radio wave processing unit 6 to convert the radio waves into a signal that can measure the arrival angle, and then estimates and outputs the arrival angle. The IMU 3 includes at least a three-axis gyro and a three-axis accelerometer, and outputs the measured angular velocity and acceleration. The absolute coordinate output unit 4 includes, for example, a GPS (Global Positioning System), and outputs the absolute coordinates on Earth of the mobile body to which the GPS is attached, such as latitude, longitude, and altitude. This may be a GNSS receiver other than GPS, or if the mobile station's range of movement is extremely narrow, it may be a memory in which fixed values are stored.

The attitude and orientation calculation unit 1 receives input from these outputs and estimates the attitude using a filter. The filter receives input and repeatedly performs estimation in discrete time, and has an updated value for the previous step and a predicted value for the new step. This is used together with new data input to proceed with the estimation. Hereinafter, there may be cases where the terms "estimated" and "estimated value" are used without distinguishing between updating and prediction. Specifically, the estimation process is represented by the following process.

The direction of gravity is calculated by subtracting the predicted linear acceleration from the input acceleration data, and the direction of gravity is calculated from the predicted posture, and the difference vector between these (first difference vector, gravity vector difference) is calculated.

Next, the satellite direction, azimuth and elevation angle are calculated from the device's absolute coordinate data and pre-stored satellite orbit information. The calculated satellite direction is a direction defined on the earth's horizon (hereafter referred to as the global frame), not a direction defined on the antenna plane. A satellite direction vector defined on the antenna plane is calculated from the calculated satellite direction and estimated attitude, and the difference vector (second difference vector, arrival angle vector difference) between this and the satellite direction vector calculated from the input arrival angle data is calculated.

The two difference vectors, the first difference vector and the second difference vector, contain information about the difference between the current estimated posture and the actual posture. However, because they are generated from measured values that contain a lot of noise, it is necessary to apply an appropriate filter. Note that a filter that estimates a response from the difference between two series of variables with different frequency responses in this way is called a complementary filter.

This algorithm is processed in discrete time. Furthermore, the posture to be estimated cannot be measured directly. A typical filter for estimating such a state that cannot be directly observed in discrete time is the Kalman filter. Below, we will explain using the Kalman filter as an example, but the filter used in this disclosure is not limited to the Kalman filter, and other filters having similar functions may be used.

The states in the Kalman filter are the attitude angle error, gyro bias error, and linear acceleration error. The angle of arrival is not disturbed by the previous state, unlike magnetism, and there are only errors due to noise or errors close to outliers similar to fading fluctuations, but if there is enough visibility to determine the angle of arrival, the errors are basically random. Therefore, since there is no need for a state equivalent to magnetic disturbances such as angle of arrival deviation, the states are the same three errors as when there is no magnetism. The state equation is the same as the state equation listed in the literature, for example, MATLAB Sensor fusion and tracking toolbox, imufilter, https://jp.mathworks.com/help/fusion/ref/imufilter-system-object.html, so the state covariance is also the same. On the other hand, the various equations related to the measured values are different. Only the different parts are described below. For other parts, please refer to the above literature. The notation used for variables, etc. is similar to that used in the document "Freescale Sensor Fusion Kalman Filter (fusion.c) Technical Note," https://github.com/memsindustrygroup/Open-Source-Sensor-Fusion/tree/master/docs, but this does not affect the scope of rights in this disclosure.

In the following formulas, + in the upper right corner is the update value, - is the predicted value, and k in the lower right corner is the sample number. G in the upper left corner indicates the global frame, and S indicates the sensor frame (the frame defined on the antenna plane). T stands for transposition.

The measurement value ^S z _ε,k can be expressed as follows:

^S g^ _G,k is the gravity direction vector calculated from the current attitude, and ^S g^ _A,k is the gravity direction vector obtained by subtracting the linear acceleration from the acceleration measurement value. ^S u^ _G,k is the satellite direction vector calculated from the current attitude direction, absolute coordinates, and satellite orbit information, and ^S u^ _SL,k is the satellite direction vector calculated from the measured arrival angle. Gravity is expressed in units of [g]. Additionally, angles and each speed are basically in units of degrees or dps. If the sample interval is δt, the observation matrix that converts the predicted value to the state can be expressed as follows:

α = π / 180, _I3 is the 3 × 3 identity matrix, _O3 is the 3 × 3 zero matrix, and (ω×) can be expressed as follows, where ω = [ω _x , ω _y , ω _z ].

The observation noise covariance can be expressed as follows:

Q _vA is the acceleration noise variance [g ² ], Q _wA is the linear acceleration noise variance [g ² ], Q _vG is the gyroscope noise variance [(deg/s) ² ], Q _wb is the gyroscope drift noise variance [(deg/s) ² ], and Q _vSL is the noise variance of the angle of arrival.

Note that since the states are the same as in the MATLAB article, the state covariance can also be the same as in the MATLAB article. However, the state covariance differs slightly depending on the article. In the MATLAB article, each 3 × 3 block in the matrix contains only diagonal elements, but it is acceptable for the state covariance to include off-diagonal elements as in the Freescale article.

The other parts are basically the same as in the MATLAB document, so they will be omitted. Since the error of the parameter to be estimated is set as the state, it is the error, which is the state, that is estimated by the Kalman filter update equation. These are used to correct the predicted values of attitude, gyro bias, and linear acceleration to obtain updated values. The method of calculating the next step predicted values for these values is basically the same as in the above MATLAB document.

The attitude and orientation calculation unit 1 outputs the attitude or attitude and orientation calculated in this way.

The results of this disclosure are shown in the form of simulation results. The route is as follows: the first 1/3 of the journey is a 5.14 degree descent, the last 1/3 is a 5.14 degree ascent, and the middle is flat, about 3000m long, traveled at 40km/h. The vehicle stops at the beginning and end, and turns 90 degrees right almost in the middle, but stops once before that before starting off.

Figure 2 shows the estimated progress of X and Y rotations, which indicate attitude, among the attitude orientations. The dashed line in the upper diagram indicates the correct value, and the solid line is a comparative example using the algorithm in the MATLAB literature. The comparative example works sufficiently, but there is an error of 1 degree or more in the Y rotation. When the allowable error in polarization is within ±1.8 degrees, and taking into account errors that occur in the antenna, it cannot be said that an error of 1 degree or more on one rotation axis is sufficiently small. The lower diagram plots the estimated results of the comparative example in the upper diagram and this embodiment. The thick line is this embodiment, and the thin line is the comparative example, and the attitude error is clearly reduced by this embodiment.

In this embodiment, a low-pass filtering model is adopted in which the linear acceleration prediction value ^S a^ ^-k ₊₁ is predicted by multiplying the update value of the previous step by a coefficient c _a in accordance with the MATLAB literature.

In the MATLAB literature, the coefficient value is a fixed value, and the default is 0.5. However, in this model, when the actual linear acceleration increases due to acceleration/deceleration or rotation, the estimated linear acceleration value does not keep up with the actual linear acceleration. Therefore, it is advisable to increase the coefficient value in situations where it is determined that the linear acceleration is increasing. Specifically, it is advisable to monitor the gyro measurement value, and increase c _a to approach 1 when, for example, significant rotation is recognized by threshold judgment, when the estimated linear acceleration value also exceeds the threshold, or when it is determined that there is acceleration/deceleration or rotation by combining the measured angular velocity and acceleration.

The measurement values from the two difference vectors are ultimately allocated to the attitude angle error, gyro bias error, and linear acceleration error, but if the arrival angle measurement happens to be close to the angle before the turn, such as when there is a sharp turn with a small radius, it may not be possible to keep up with the turn. Since this algorithm works even if there are some arrival angle measurements missing, in such cases it is advisable to reduce the contribution of the arrival angle difference vector. Specifically, it is advisable to increase the Q _vSL of the observation noise covariance.

In this embodiment, there is no particular restriction on the method of estimating the satellite's arrival angle. Any commonly used method may be used. Since the number of elements in a phased array antenna in a mobile phone is about several, it is not uncommon to detect the phase and amplitude of all elements and estimate the arrival angle from them. However, in satellite communications, especially communications with geostationary satellites, the beam width is very narrow, so the antenna aperture is large, and the frequency often exceeds 10 GHz, so in the case of a phased array, the element spacing is narrow, and as a result, the number of elements becomes several thousand, making it difficult to detect the phase and amplitude of all elements. In many cases, the arrival angle is estimated by detecting it by electronically or mechanically swinging the beam, dividing the entire antenna into several pseudo parts, or forming multiple receiving beams simultaneously. Any of these methods can be applied. Since the accuracy and measurement frequency differ depending on the method, it is recommended to change the filter constants and the like according to each method.

As in this embodiment, the method of inputting measurement data from multiple systems into a filter as measured values and estimating the state is called tight coupling. A method of estimating the state from each system individually and combining the estimated states to estimate the total state is called loose coupling, but it is known that tight coupling has smaller errors than loose coupling.

In addition, compared to using magnetism for the second difference vector, the angle of arrival of satellite radio waves is very accurate, so the resulting attitude error can be made very small.

Second embodiment
The second embodiment is a modification of the first embodiment. Fig. 3 is a diagram showing an implementation according to one embodiment. In addition to the configuration of the above-mentioned embodiment, the attitude and orientation estimation device 100 includes a visibility determination unit 7. Note that other configurations and other data flows are the same as those in Fig. 1, and therefore some parts are omitted from the illustration.

In satellite communications, if there is an obstacle such as a building between the satellite and the earth station, it is not possible to receive radio waves from the satellite. Even if the radio waves are not completely blocked, if they are partially blocked by street trees or the like, it may not be possible to ensure a sufficient SNR (Signal to Noise Ratio) for communications. In such cases, the angle of arrival may not be able to be measured, or the measured angle of arrival may be an outlier. These conditions are said to mean that line of sight to the satellite cannot be secured, or line of sight has been lost, and the angle of arrival cannot be calculated.

In addition, while the IMU measurement frequency is generally once every few to several tens of milliseconds, the arrival angle measurement frequency is often less than this. The frequency of data acquisition from the IMU can be set to match the arrival angle estimation frequency, but if the arrival angle estimation frequency becomes frequent, such as every second, the movement of the moving object will be much faster than that, and there is a high possibility that necessary rotation and acceleration will be missed. Therefore, it is desirable to continue processing the data from the IMU even if the arrival angle estimation result has not been input.

In this case, the difference vector (second difference vector) that uses the arrival angle vector to the satellite is not used, and processing continues using only the first difference vector. The operation at this time is the same as that described in the MATLAB literature.

The presence or absence of visibility input from the presence or absence of visibility judgment unit 7 is added as an input to the attitude and direction calculation unit 1. The presence or absence of visibility judgment unit 7 receives input from the received radio wave processing unit or the arrival angle estimation unit (not shown) and judges the presence or absence of visibility. Specifically, it receives input such as power, SNR, CNR (Carrier to Noise Ratio), reception success or failure due to error detection when receiving a packet, and estimated attitude output from the received radio wave processing unit, and verifies the angle difference with the arrival angle of the previous sample, and furthermore, whether there is a significant deviation between the change in attitude and the change in the arrival angle, and judges whether the arrival angle is reliable and whether the estimated arrival angle has sufficient accuracy and outputs it. At the same time, it receives the arrival angle input from the arrival angle estimation unit, and if there is insufficient visibility, stops outputting the arrival angle. If it is judged to be halfway reliable, a threshold value or the like may be set to switch whether or not to output, or the arrival angle may be output simultaneously with the reliability information (SNR, etc.). The filter may adjust the contribution of the arrival angle to the update value by weighting the reliability of the arrival angle according to the SNR, etc. The Kalman filter applies a process in which the observation noise covariance for the arrival angle is increased when the reliability is low.

When the SNR is high, the angle of arrival error is relatively close to a normal distribution, but when visibility is unstable, outliers are often produced. Since the Kalman filter does not work well with error distributions other than normal distribution, it is desirable not to use angle of arrival data that is clearly an outlier, or to use it with a lower weight. Therefore, this embodiment makes it possible to perform continuous, good attitude calculations.

The attitude and orientation calculation unit 1 receives input on whether or not there is line of sight, and if there is line of sight and a good angle of arrival is obtained, it performs the same processing as in Figure 1. However, if there is no line of sight, or if there is no angle of arrival data due to a problem with the timing of IMU data acquisition, it performs processing using only the difference vector related to gravity.

Specifically, the measurement value of equation (1) is as follows:

The observation matrix is as follows:

The observation noise covariance is as follows:

The matrix size will change depending on the option you choose, but the algorithm used for processing will not change. In other words, instead of switching algorithms, you can simply change the size of the parameters used without changing the set of algorithms.

This allows for continuous attitude updates even when line of sight to the satellite is lost or when timing issues mean new angle-of-arrival data is unavailable.

As explained in the conventional technology, the accuracy of the attitude gradually deteriorates due to the disappearance of the second difference vector. Since the deterioration is mainly caused by the angle drift due to the gyro bias, the arrival angle estimation is performed about once per second, and if the gyro is an automotive-grade calibrated IMU, a significant attitude error does not usually occur. On the other hand, when the line of sight to the satellite is lost for a long period of time, such as inside a long tunnel, the attitude error becomes an amount that cannot be ignored. However, since the purpose of correctly detecting the attitude is to transmit radio waves to the satellite with the correct polarization, it is not a problem even if the attitude is slightly shifted in places such as inside a tunnel where radio waves do not reach the satellite in the first place and therefore no transmission is performed. At the same time, tracking of the satellite direction based on the arrival angle is also impossible, so the satellite direction will continue to be updated using the change in the estimated attitude angle. In this case, it is sufficient that the deviation in the attitude direction is small enough that the search for the satellite direction can be completed in a short period of time when the line of sight to the satellite is restored after passing through the tunnel.

Third embodiment
FIG. 4 is a diagram showing a schematic diagram of an example of a satellite communication earth station 200 according to an embodiment. This is a satellite communication earth station including an attitude and orientation estimation device of the present invention. Explanation of parts common to the first embodiment will be omitted. The satellite communication earth station 200 includes, for example, a receiving antenna 8, a transmitting antenna 9, a beam control unit 12 constituting an antenna control unit 11, a polarization calculation unit 13, and a filter 14 in addition to the configuration of the first embodiment.

The satellite communication earth station 200 according to this embodiment is configured to include a portion for transmitting radio waves to a satellite. According to this embodiment, it is possible to calculate the transmission polarization using an attitude estimated with high accuracy. The antenna control unit 11 receives input of the attitude from the attitude and direction calculation unit 1. In addition, it receives input of the absolute coordinates from the absolute coordinate output unit 4 and the arrival angle from the arrival angle measurement unit 2.

The output of the angle-of-arrival measurement unit may contain large errors due to noise, so the angle of arrival may be filtered by filter 14 to suppress noise. It is preferable to use a filter that allows real-time prediction, such as a Kalman filter or an α-β filter.

The polarization calculation unit 13 calculates the polarization in the global frame, that is, the polarization when the antenna plane is perfectly horizontal, from the satellite orbit information, the polarization angle to be transmitted, and the absolute coordinate information of the earth station. Since the earth station is almost never perfectly horizontal, it uses the input attitude direction to convert it into the polarization defined in the antenna plane (sensor frame) and outputs it to the beam control unit 12. The beam control unit 12 receives inputs of the highly accurate arrival angle and polarization with noise suppressed, and controls the transmitting antenna 9 so that the azimuth, elevation angle, and polarization of the transmitted beam match them. If the transmitting antenna is a phased array antenna, it controls the phase and amplitude of each element.

The transmitting antenna 9 receives a transmission signal that has been appropriately modulated and shaped by the RF section or other unit to have the appropriate frequency, spectrum, and power, and the signal is then sent out from the transmitting antenna 9 in the appropriate direction and polarization.

By applying this embodiment, it is possible to estimate polarization with higher accuracy. As a result, when the allowable polarization error is specified, the range of allowable error in other parts, such as the antenna control unit and the antenna itself, increases, easing the difficulty of design and manufacturing.

In addition, from the satellite orbital information and the absolute coordinates of the earth station, not only the polarization but also the direction in the satellite's global frame, i.e., the azimuth and elevation angle, can be calculated. By matching the estimated attitude to this, it can be converted to the azimuth and elevation angle in the sensor frame. On the other hand, the estimated arrival angle in the sensor frame is also obtained by filtering the arrival angle measurement result, but in many cases, the azimuth and elevation angles of the two do not match completely. In this case, it is desirable to calculate the polarization according to the azimuth and elevation angle that are considered to be more accurate. However, the arrival angle estimate does not include all the information about the attitude. Even if the transmitting antenna is rotated around the line connecting the transmitting antenna 9 and the satellite 300 as the rotation axis as shown in Figure 5, the measured azimuth and elevation angle do not change. On the other hand, if the antenna is rotated in this way when it outputs a fixed linearly polarized wave, the polarization seen from the satellite changes corresponding to the rotation. Therefore, the polarization cannot be determined only from the arrival angle estimation result.

The arrival angle filtering by filter 14 can optimize parameters specialized for estimating the arrival angle, so there is a high possibility that the error will be smaller than the azimuth and elevation angle calculated by attitude and orientation calculation unit 1 as part of the attitude and orientation. Of course, if the parameters of the attitude and orientation calculation unit are fully optimized and the azimuth and elevation angle calculated from the attitude and orientation can be calculated with extremely high accuracy, then the azimuth and elevation angle can be trusted.

Assuming that the filtered arrival angle estimation result has higher accuracy, a method of absorbing these differences will be described as a modification of the third embodiment. First, the azimuth and elevation angle are calculated from the attitude and orientation calculated by the attitude and orientation calculation unit 1, the absolute coordinates, and the satellite orbit information. The attitude and orientation are corrected so that the roll angle of the antenna surface on the global frame does not change so that this matches the filtered arrival angle estimation result. In other words, the azimuth and elevation angle in the global frame of the attitude and orientation are corrected. First, the attitude and orientation are converted to the global frame, and the difference in azimuth with the arrival angle converted to the global frame is calculated, and the attitude and orientation are corrected so that this difference is eliminated. Next, the difference in elevation angle is calculated in the same way, and the attitude and orientation are corrected so that this difference is eliminated. The polarization to be output by the antenna is calculated from the corrected attitude and orientation, the absolute coordinates, the satellite orbit information, and the polarization used.

By doing this, it becomes possible to perform polarization estimation with higher accuracy.

Figure 6 is a flowchart showing the processing in the first embodiment as an example of the processing of the attitude and orientation estimation device 100 or the satellite communication earth station 200 in each of the embodiments described above. The processing flow for other embodiments can also be read from the drawing as shown in the figure.

The attitude and orientation calculation unit 1 predicts the attitude angle, gyro bias, and linear acceleration (S100). The attitude and orientation calculation unit 1 predicts the attitude angle, gyro bias, and linear acceleration, for example, based on the angular velocity acquired from the IMU 3, using an algorithm in the MATLAB literature.

The attitude and orientation calculation unit 1 calculates the gravity vector difference (first difference vector) and the arrival vector difference (second difference vector) (S102). The attitude and orientation calculation unit 1 calculates the first difference vector and the second difference vector based on, for example, the acceleration acquired from the IMU 3, the absolute coordinates acquired from the absolute coordinate output unit 4, and the arrival angle acquired by the arrival angle measurement unit 2.

The attitude/orientation calculation unit 1 calculates, for example, a first difference vector, which is the difference vector between the direction of gravity obtained by subtracting the linear acceleration predicted in S100 from the input acceleration data and the direction of gravity calculated from the predicted attitude.

The attitude and orientation calculation unit 1 also calculates a second difference vector, which is the difference vector between the satellite direction corrected based on the attitude predicted from the satellite direction obtained from the absolute coordinate data of the device and the satellite orbit information stored in advance, and the satellite direction calculated from the input arrival angle data.

The attitude and orientation calculation unit 1 updates the attitude angle error, the gyro bias error, and the linear acceleration error (S104). For example, when a Kalman filter is used, this error update is implemented by estimation using the update equation for the state of the Kalman filter.

The attitude and orientation calculation unit 1 updates the attitude angle, gyro bias, and linear acceleration using the errors acquired in S104 (S106).

If the operation is to be ended (S108: YES), the process is completed after appropriate processing. If the operation is to be continued (S108: NO), the process is repeated from S100. At this time, the value calculated in the previous iteration may be appropriately converted using a coefficient, etc.

Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the gist of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are included in the scope of the invention and its equivalents described in the claims.

100: Posture orientation estimation device,
1: Attitude and orientation calculation unit,
2: Angle of arrival measurement unit;
3: Inertial Measurement Unit,
4: Absolute coordinate output section,
5: Antenna,
6: Receiving radio wave processing section,
7: Visibility determination section,
8: Receiving antenna,
9: Transmitting antenna,
11: Antenna control section,
12: Beam control section,
13: Polarization calculation section,
14: Filter,
200: Satellite communication earth station,
300: Satellite

Claims (10)

an arrival angle measuring unit that receives radio waves transmitted from a satellite and measures an arrival angle of the received radio waves;
an inertial measurement unit including at least an accelerometer and an angular velocity meter; and an absolute coordinate output unit that acquires absolute coordinates of a current location;
an attitude orientation calculation unit that calculates a gravity direction measurement value by subtracting a linear acceleration estimated in advance from the acceleration measured by the accelerometer, generates a first difference vector which is a difference from the gravity direction estimated from the estimated attitude in advance, generates a second difference vector which is a difference between the measured arrival angle acquired by the arrival angle measurement unit and the arrival angle estimated from the absolute coordinates, the orbital information of the satellite, and the estimated attitude in advance, and updates the estimated attitude in advance using a complementary filter based on the first difference vector and the second difference vector to acquire an estimated attitude;
An attitude and orientation estimation device comprising: The complementary filter estimates an attitude, a linear acceleration, and an angular velocity bias from the first difference vector and the second difference vector using a Kalman filter.
2. An attitude and orientation estimation device according to claim 1. The complementary filter estimates an attitude error, a linear acceleration error, and an angular velocity bias error as states, and estimates the attitude, the linear acceleration, and the angular velocity bias based on the states.
3. The posture direction estimation device according to claim 2. the arrival angle estimation unit notifies the attitude and orientation calculation unit when visibility to the satellite is lost;
The attitude/orientation calculation unit continues processing using the first difference vector.
2. An attitude and orientation estimation device according to claim 1. determining that the line of sight to the satellite has been lost when at least one of the strength of the radio wave received from the satellite, the signal-to-noise ratio, or the carrier-to-noise ratio becomes smaller than a threshold value;
5. An attitude and orientation estimation device according to claim 4. The absolute coordinate output unit is a GNSS (Global Navigation Satellite System) receiver.
2. The posture direction estimation device according to claim 1. an arrival angle measuring unit that receives radio waves transmitted from a satellite and measures an arrival angle of the received radio waves;
an inertial measurement unit including at least an accelerometer and an angular velocity meter; and an absolute coordinate output unit that acquires absolute coordinates of a current location;
an attitude orientation calculation unit that calculates a gravity direction measurement value by subtracting a linear acceleration estimated in advance from the acceleration measured by the accelerometer, generates a first difference vector which is a difference from the gravity direction estimated from the estimated attitude in advance, generates a second difference vector which is a difference between the measured arrival angle acquired by the arrival angle measurement unit and the arrival angle estimated from the absolute coordinates, the orbital information of the satellite, and the estimated attitude in advance, and updates the estimated attitude in advance using a complementary filter based on the first difference vector and the second difference vector to acquire an estimated attitude;
an antenna that outputs radio waves to the satellite;
an antenna control unit that determines a direction of an output beam of the antenna based on the measurement angle measured by the arrival angle measurement unit, and determines a polarization of the output beam using at least the estimated attitude and the direction of the output beam;
A satellite communications earth station comprising: The antenna controller determines the direction of the output beam by applying a filter process to the angle of arrival.
A satellite communications earth station according to claim 7. the antenna control unit, when the absolute coordinates and the beam direction estimated from the estimated attitude do not match the direction of the output beam, estimates a corrected attitude by correcting the estimated attitude with respect to a yaw angle and a pitch angle, and determines a polarization of the output beam from the corrected attitude and the direction of the output beam.
Satellite communications earth station. an arrival angle measuring unit that receives radio waves transmitted from a satellite and measures the arrival angle of the received radio waves;
The absolute coordinate output unit obtains the absolute coordinates of the current location,
An attitude and orientation calculation unit calculates a gravity direction measurement value by subtracting a linear acceleration estimated in advance from the acceleration measured by the accelerometer,
the attitude/orientation calculation unit generates a first difference vector which is a difference between a direction of gravity estimated from a previously estimated attitude,
the attitude/orientation calculation unit generates a second difference vector which is a difference between the measured arrival angle acquired by the arrival angle measurement unit and an arrival angle estimated from the absolute coordinates, the satellite orbit information, and the prior estimated attitude;
the attitude/orientation calculation unit updates the prior estimated attitude with a complementary filter based on the first difference vector and the second difference vector to obtain an estimated attitude;
Attitude and heading estimation method.