Millimeter wave air-to-air beam tracking method based on communication perception integration

Abstract

The invention discloses a millimeter wave air-to-air beam tracking method based on communication perception integration, and relates to a millimeter wave air-to-air beam tracking method. The invention aims to solve the problems of high training overhead, high hardware complexity and insufficient target parameter estimation precision in a large-scale antenna array beam tracking process in a high dynamic scene. A sending end designs an analog beam forming vector to send a communication signal according to a predicted position of a receiving end; the sent communication signal is received by a receiving end to realize communication transmission, and is reflected by the receiving end to form a sensing echo; a receiver at the transmitting end receives and processes the echo signal by adopting an interlaced hybrid antenna array, and performs two-step parameter estimation on the angle of arrival, distance and radial speed of the target in a symbol domain; and then predicting the state of the receiving end based on the relative motion model in combination with an unscented Kalman filter to realize the dynamic tracking of the beam. The invention belongs to the technical field of wireless communication.

Description

Technical Field

[0001] This invention relates to a millimeter-wave air-to-air beam tracking method, belonging to the field of wireless communication technology. Background Technology

[0002] With the increasing application of high-altitude small aircraft in emergency communication relay, long-distance logistics transportation, and border patrol, the demand for high-altitude inter-aircraft communication is becoming increasingly prominent. These aircraft typically operate in the mid-to-high altitude region, between approximately 1,000 and 6,000 meters. Due to the distance from ground base stations at this altitude, and limitations imposed by the Earth's curvature and antenna elevation angles, stable coverage via terrestrial networks is difficult to achieve. While satellite communication offers wide coverage, it suffers from high latency and high cost, making it difficult to meet the requirements for low latency and high throughput. Therefore, direct communication between high-altitude aircraft has become a crucial means of ensuring data transmission.

[0003] High-altitude inter-aircraft communication demands high link stability and transmission rates, typically employing millimeter-wave bands combined with directional beams to achieve high-speed and highly reliable transmission. However, in high-altitude, high-speed motion scenarios, directional beams are highly susceptible to misalignment. Once misalignment occurs, re-establishing beam alignment requires significant time and communication resource overhead, impacting communication performance. Therefore, beam tracking technology is crucial. Existing beam tracking methods mainly fall into two categories: beam training-based and beam prediction-based. The former maintains the link through periodic scanning but consumes substantial time and communication resources; the latter relies on external sensing equipment to locate the aircraft and predict the beam direction, reducing training overhead but requiring additional hardware support, thus increasing system cost and complexity.

[0004] In recent years, with the development of integrated communication and sensing technologies, professionals have attempted to utilize the echo of communication signals to achieve sensing functions, thereby assisting beam tracking without introducing additional sensing equipment. This approach can reduce beam training overhead and improve resource utilization. Among existing methods, professionals have proposed using matched filtering to process the echo signal to estimate target parameters. However, this method directly performs matched filtering on the transmitted waveform, which requires configuring a separate sensing processing link, increasing system hardware complexity and energy consumption. Furthermore, due to the poor correlation of the communication waveform itself, the estimation accuracy is limited, making it difficult to achieve reliable beam tracking in highly dynamic scenarios.

[0005] Furthermore, most current beam tracking methods based on integrated communication and sensing employ fully digital antenna arrays to receive echo signals. However, in large-scale antenna array scenarios, demodulating and processing each antenna element individually is not only complex but also places enormous pressure on chip area, power consumption, and heat dissipation management, especially when processing broadband signals in the millimeter-wave band. These issues limit the effectiveness and widespread application value of existing beam tracking methods in direct communication between small high-altitude aircraft. Summary of the Invention

[0006] To address the problems of high training overhead, high hardware complexity, and insufficient target parameter estimation accuracy in large-scale antenna array beam tracking under high dynamic scenarios, this invention proposes a millimeter-wave air-to-air beam tracking method based on integrated communication and sensing.

[0007] The technical solution adopted by the present invention to solve the above problems is as follows: The steps of the present invention include: Step 1: The transmitting end establishes a communication link with the receiving end through initial beam training, and in the process, obtains the angle between the fixed flight directions of both parties and the initial state variables; Step 2: Based on the relative motion model of the high-altitude aircraft, the transmitting end uses an unscented Kalman filter to predict the system state variables and obtain the predicted values ​​of the state variables at the next moment. Step 3: The transmitting end transmits a communication signal to the receiving end based on the predicted state variables. The communication signal is received by the receiving end for normal communication transmission, and is also reflected by the receiving end to form a sensing echo, which is received by the interleaved hybrid antenna array of the transmitting end for subsequent sensing processing. Step 4: The transmitting end demodulates the received echo signal into the symbol domain and estimates the target's elevation and azimuth angles by combining the subarray correlation characteristics of the interleaved hybrid antenna array. Step 5: Based on the obtained angle estimation results, the transmitting end performs digital combining processing on the echo signal and executes two-dimensional matched filtering to jointly estimate the relative distance and radial velocity of the target. Step 6: Based on the established observation model, the transmitting end updates the current state variables using an unscented Kalman filter; Step 7: Repeat steps 2 to 6. While communicating with the receiver, the transmitting end continuously performs dynamic beam tracking on the receiver to ensure the continuity and stability of the inter-machine communication link.

[0008] Furthermore, step 2 specifically includes: Step 201: When the observation interval is less than the predetermined value, the aircraft moves at a constant linear speed at a fixed altitude, and a state evolution model of the relevant state variables is established. Step 202: Based on the state variables and covariance matrix of the previous time step, generate the following using Cholesky decomposition. Sigma points, It is the dimension of the state variables; Step 203: Substitute each Sigma point into the state evolution model to generate the prediction point at the current time, and calculate the weighting coefficient of each prediction point according to the sampling strategy. Generate the prediction state variable and prediction covariance matrix based on the weighting coefficient.

[0009] Furthermore, step 3 specifically includes: Step 301: The transmitting end maps the communication data to a time-delay-Doppler grid, obtains the time-frequency domain symbol through inverse symmetric Fourier transform, and then obtains the baseband transmission signal through Heisenberg transform; Step 302: Based on the predicted state variables obtained in step 2, the transmitting end calculates the estimated elevation angle and azimuth angle of the receiving end and constructs the transmission simulation beamforming vector. Step 303: The sending end transmits data to the receiving end in units of frames. The transmitted communication signal is transmitted through the line-of-sight channel. On the one hand, it is received by the receiving end for service transmission, and on the other hand, it is reflected by the surface of the receiving end to form a sensing echo. Step 304: The receiver at the transmitting end uses an interleaved hybrid antenna array to receive and converge the echo signals to obtain a received data vector for subsequent sensing processing.

[0010] Furthermore, step 304 specifically includes: The receiver's antenna array size is consistent with that of the transmitting antenna, employing an interleaved subarray structure, dividing the uniform planar antenna array into... Subarrays, and They are along shaft and The number of subarrays of the axis, each subarray containing There are several antenna array elements; each antenna element is connected to a phase shifter, and each subarray is connected to an RF chain; the phase shifter settings of each subarray are kept consistent to obtain the index. The received signal of the subarray.

[0011] Furthermore, step 4 specifically includes: Step 401: Remove the cyclic prefix from the received signal of each subarray and perform matched filtering. Sample the signal according to the symbol time slot and subcarrier interval to obtain the time-frequency domain symbol. Then map the signal to the time-delay-Doppler symbol domain through the Sine Fourier transform to obtain the relationship between the received symbol and the transmitted symbol. Step 402: Rearrange the received symbols in the same time slot into a matrix according to the subarray dimension, where each column corresponds to a received symbol of a subarray; Step 403: Divide the symbol field receiving matrix into subarrays, perform cross-correlation operations on the column vectors of adjacent subarrays along the x-axis and z-axis respectively, and then accumulate them; Step 404: Extract the cross-correlation parameters and calculate the estimated values ​​of the pitch and azimuth angles.

[0012] Furthermore, step 5 specifically includes: Step 501: Construct a digital merging vector based on the angle estimate obtained in Step 4; Step 502: Based on the symbol domain input-output model and the characteristics of the Dirichlet kernel, define the phase correction term; perform a two-dimensional cross-correlation between the received data and the transmitted symbols to form the time delay-Doppler cumulative statistics; Step 503: Find the maximum value of the time delay-Doppler cumulative statistic, and use its corresponding index as the estimate of time delay and Doppler. Convert the raster index into time delay and Doppler frequency shift.

[0013] Furthermore, step 6 specifically includes: Step 601: Establish an observation model based on the estimated values ​​of elevation and azimuth obtained in Step 4 and the estimated values ​​of time delay and Doppler shift obtained in Step 5; Step 602: Based on the predicted state variables and covariance matrix, generate the following using Cholesky decomposition: One Sigma point; Step 603: Substitute each Sigma point into the observation model to generate the predicted values ​​of the observed variables, the predicted covariance matrix, and the state-observation cross-covariance matrix; Step 604: Calculate the Kalman gain and update the state variables and covariance matrix at the current time step.

[0014] The beneficial effects of this invention are as follows: This invention employs an interleaved hybrid antenna array, multiplexing the communication receiving link to achieve sensing functionality, eliminating the need for a separate sensing processing link, thereby effectively reducing hardware complexity, system cost, and energy consumption. By estimating target parameters in the symbol domain and combining a high-altitude vehicle relative motion model with unscented Kalman filtering to achieve state prediction and updating, dynamic and accurate beam tracking can be achieved, reducing beam training overhead and improving tracking accuracy and stability. Simultaneously, this method maintains good robustness in high-dynamic, high-Doppler environments, thus ensuring the continuity and reliability of the communication link. Attached Figure Description

[0015] Figure 1 This is a model diagram of the millimeter-wave air-to-air communication and sensing integrated system of the present invention; Figure 2 This is a schematic diagram of the dynamic geometric relationship of the high-altitude small aircraft of the present invention; Figure 3a This is a diagram showing the real-time pitch angle tracking results of the present invention; Figure 3b This is a diagram showing the real-time azimuth tracking results of the present invention. Figure 4a This is a comparison chart of the root mean square error of pitch angle tracking in this invention; Figure 4b This is a comparison chart of the root mean square error of azimuth tracking in this invention. Detailed Implementation

[0016] Specific implementation method one: as follows Figure 1 As shown, a millimeter-wave air-to-air beam tracking method based on integrated communication and sensing includes the following specific steps: Step 1: The transmitting end establishes a communication link with the receiving end through initial beam training, and in the process, obtains the angle between the fixed flight directions of both parties. and initial state variables Where the subscript 0 indicates the initial time, and This indicates the elevation and azimuth angles of the receiving end relative to the transmitting end. Indicates relative distance. Indicates the speed of the receiving end's flight; Step 2: Based on the relative motion model of the high-altitude aircraft, the transmitting end uses an unscented Kalman filter to predict the system state variables and obtain the predicted values ​​of the state variables at the next moment. Step 3: The transmitting end transmits a communication signal to the receiving end based on the predicted state variables; the communication signal is received by the receiving end for normal communication transmission, and is also reflected by the receiving end to form a sensing echo, which is received by the interleaved hybrid antenna array of the transmitting end for subsequent sensing processing. Step 4: The transmitting end demodulates the received echo signal into the symbol domain and estimates the target's elevation and azimuth angles by combining the subarray correlation characteristics of the interleaved hybrid antenna array. Step 5: Based on the obtained angle estimation results, the transmitting end performs digital combining processing on the echo signal and executes two-dimensional matched filtering to jointly estimate the relative distance and radial velocity of the target. Step 6: Based on the established observation model, the transmitting end updates the current state variables using an unscented Kalman filter, and combines the fusion results of prediction and observation to effectively correct the current state of the system. Step 7: Repeat steps 2 to 6. While communicating with the receiver, the transmitting end continuously performs dynamic beam tracking on the receiver to ensure the continuity and stability of the inter-machine communication link.

[0017] Specific Implementation Method Two: Based on Specific Implementation Method One, the specific process of the transmitting end predicting the state variables based on the relative motion model in step 2 is as follows: Step 201: Consider that once the two communicating parties establish a communication connection, they will be in a relatively stable state for a period of time afterward, such as... Figure 2 As shown, when the observation interval When the difference is small, the aircraft can be considered to be at a fixed altitude difference. If the motion is uniform in a straight line, then we have the following regarding the state variables. State evolution model: (1) Among them, subscript Indicates different times, , , , It is the flight speed of the transmitting end, which can be obtained through its own control system; Step 202: Based on the state variables of the previous time step Covariance Matrix Generate using Cholesky decomposition Sigma points, It is the dimension of the state variables; Step 203: Substitute each Sigma point into the state evolution model to generate the prediction point at the current time, calculate the weighting coefficient of each prediction point according to the sampling strategy, and generate the prediction state variable based on the weighting coefficient. and predict covariance matrix .

[0018] Specific Implementation Method Three: Based on Specific Implementation Method One, the communication signal sent based on the predicted state variables in step 3 is received by the receiving end and reflected by the receiving end to form a sensing echo. The specific process is as follows: Step 301: The transmitting end maps the communication data to a delay-Doppler grid to obtain... The time-frequency domain symbol is obtained by inverse symplectic Fourier transform, and then the baseband transmitted signal is obtained by Heisenberg transform. ; Step 302: The sending end uses the predicted state variables obtained in step 2. The estimated elevation and azimuth angles of the receiver are calculated. Constructing a simulated beamforming vector for transmission ,have (2) in, and These represent the transmitting antenna array along... shaft and The number of array elements of the axis, It is the steering vector of a half-wavelength uniform planar antenna array, and its definition is as follows: (3) in, The vector represents the first One element; Step 303: The transmitting end transmits data to the receiving end in frames. The transmitted communication signal is transmitted through the line-of-sight channel. On the one hand, it is received by the receiving end for service transmission, and on the other hand, it is reflected by the surface of the receiving end to form a sensing echo. ,have (4) in, It is the complex channel gain. It is the round-trip time delay. It's the speed of light. It's a Doppler shift. It is the first The relative radial velocity at time t. It is the carrier wavelength; Step 304: The receiver at the transmitting end uses an interleaved hybrid antenna array to receive and converge the echo signals to obtain a received data vector for subsequent sensing processing. .

[0019] Specific Implementation Method Four: Based on Specific Implementation Method Three, in step 304, the transmitting end uses an interleaved hybrid antenna array to receive and converge the echo signal; the specific process is as follows: The receiver's antenna array size is consistent with that of the transmitting antenna, employing an interleaved subarray structure, dividing the uniform planar antenna array into... Subarrays, and They are along shaft and The number of subarrays of the axis, each subarray containing There are 1 antenna array element; each antenna element is connected to a phase shifter, and each subarray is connected to an RF chain; if the phase shifter settings of each subarray are kept consistent, then the index is 1. The received signal of the subarray can be represented as: (5) in, , , Indicates the first The first subarray The position of each antenna element It is the phase shift generated by the phase shifter, based on the predicted values ​​of the pitch and azimuth angles. To conduct the design, there are: (6) It is additive white Gaussian noise with a mean of 0 and a variance of . ; definition Then the received data vector can be represented as: (7) in, .

[0020] Specific Implementation Method Five: Based on Specific Implementation Method One, the specific process of demodulating the echo signal to the symbol domain and performing angle estimation at the transmitting end in step 4 is as follows: Step 401: Remove the cyclic prefix from the received signal of each subarray and perform matched filtering. Sample the signal according to the symbol time slot and subcarrier interval to obtain the time-frequency domain symbols. Then, map the symbols to the time-delay-Doppler symbol domain through Sine Fourier transform to obtain the received symbols. , and the sending symbol The following relationship exists: (8) (9) in, For the number of subcarriers, For signed numbers, It is the subcarrier spacing. It is the duration of the symbol. It is the sign error caused by noise. and These represent the inter-carrier interference interval and the inter-symbol interference interval, respectively. Step 402: Rearrange the received symbols in the same time slot into a matrix according to the subarray dimension. Each column corresponds to a received symbol of a subarray, with the following relationship: (10) in, , This represents the Klock inner product operation; Step 403: Receive the symbol field matrix Divide the data into subarrays, perform cross-correlation operations on the column vectors of adjacent subarrays along the x-axis and z-axis, and then sum them up: (11) (12) in, Represents the first of the matrix List; Step 404: Extract the phase of the cross-correlation quantity , The estimated values ​​of the pitch and azimuth angles are calculated using the following formulas. : (13) Specific Implementation Method Six: Based on Specific Implementation Method One, the specific process of the transmitting end combining the echo signals based on the angle estimate and estimating the relative distance and radial velocity in step 5 is as follows: Step 501: Angle estimation based on the result of step 4. Construct a digital merging vector : (14) Used for in-phase weighted combining of subarray signals, so that The maximum value is achieved when the angle estimation is correct, thereby improving the effective signal-to-noise ratio and decoupling from the angle dimension. The merged received data is as follows: (15) Step 502: Define the phase correction term based on the symbolic domain input-output model and the characteristics of the Dirichlet kernel. : (16) For received data With sending symbol Perform two-dimensional cross-correlation to generate the time-delay-Doppler cumulative statistic: (17) in, , ; Step 503, Search The maximum value, and its corresponding index, are used as estimates of the time delay and Doppler effect, i.e.: (18) Converting the raster index into time delay and Doppler frequency shift, i.e.: (19) Specific Implementation Method Seven: Based on Specific Implementation Method One, in step 6, the sending end updates the state variables based on the observation model; the specific process is as follows: Step 601, let the observed variable Based on the estimated values ​​of elevation and azimuth obtained in step 4 and the estimated values ​​of time delay and Doppler shift obtained in step 5, an observation model is established: (20) Step 602: Based on the predicted state variables Covariance Matrix Then, Cholesky decomposition is used to generate One Sigma point; Step 603: Substitute each Sigma point into the observation model to generate predicted values ​​for the observed variables. Predicting the covariance matrix and the state-observation cross-covariance matrix ; Step 604: Calculate the Kalman gain and update the state variables at the current time step. Covariance Matrix .

[0021] Table 1 Symbol Explanation Table

[0022] Example Example 1: The initial coordinates of the sending spacecraft are ,by speed toward Directional flight, receiving the initial coordinates of the aircraft. m, with speed toward Directional flight, transmission power is The size of the uniform planar antenna array is The number of subarrays is The carrier frequency is The subcarrier spacing is The number of subcarriers is The number of signs is In this embodiment, using The observation interval is used to track the receiver, and the tracking market is... .

[0023] Figure 3a , 3b The real-time tracking results of elevation and azimuth angles presented in this invention are shown. The horizontal axis represents the observation time slot, and the vertical axis represents the elevation and azimuth angles. The solid line represents the tracking result of the proposed scheme, and the dashed line represents the actual trajectory. As can be seen from the figure, the proposed scheme can track angles very well, thus providing effective assurance for the stability of the communication link.

[0024] Figure 4a , 4bThe figure compares the root mean square errors (RMSEs) of the proposed scheme with those relying solely on observation or those relying solely on angle tracking in terms of elevation and azimuth angles. The horizontal axis represents the transmit power, and the vertical axis represents the RMSEs of elevation and azimuth angles. Realized results represent the proposed scheme; dotted lines represent results relying solely on observation, and dashed lines represent results relying solely on angle tracking. As can be seen from the figure, the estimation error of the proposed scheme is smaller than the other two schemes at different transmit powers, and this advantage becomes more pronounced as the transmit power increases.

[0025] Further comparison Figure 4a and 4b It can be observed that the estimation error of the pitch angle is generally smaller than that of the azimuth angle. This is because the angle estimation algorithm used in this invention has higher sensitivity when the angle is close to 90°, thus the estimation result is relatively more accurate; however, as the angle gradually deviates from 90°, the sensitivity decreases, and the estimation error increases accordingly. 。

[0026] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent substitutions, and improvements made to the above embodiments without departing from the scope of the present invention, based on the technical essence of the present invention and within the spirit and principles of the present invention, shall still fall within the protection scope of the present invention.

Claims

1. A method for tracking millimeter wave air-to-air beam based on communication and sensing integration, characterized in that, The method for tracking the millimeter wave air-to-air beam based on the integrated communication and sensing is realized through the following steps: Step 1, the sending end establishes a communication link with the receiving end through initial beam training, and obtains the fixed flight direction angle and initial state variable of both sides in the process; Step 2, the sending end predicts the state variable of the system by using the unscented Kalman filter based on the relative motion model of the high-altitude aircraft, and obtains the predicted value of the state variable at the next time; Step 3, the sending end transmits a communication signal to the receiving end according to the predicted state variable; the communication signal is received by the receiving end for normal communication transmission, and is reflected by the receiving end to form a sensing echo, which is received by the interleaved hybrid antenna array of the sending end for subsequent sensing processing; Step 4, the sending end demodulates the received echo signal to the symbol domain, and estimates the pitch angle and azimuth angle of the target according to the subarray correlation characteristics of the interleaved hybrid antenna array; Step 5, on the basis of the angle estimation result, the sending end performs digital combining processing on the echo signal, and executes two-dimensional matched filtering, so as to jointly estimate the relative distance and radial velocity of the target; Step 6, the sending end updates the current state variable by using the unscented Kalman filter based on the established observation model; Step 7, steps 2 to 6 are repeatedly executed, and the sending end continuously realizes the dynamic beam tracking of the receiving end while communicating with the receiving end, so as to ensure the continuity and stability of the inter-aircraft communication link.

2. The method of claim 1, wherein, Step 2 specifically includes: Step 201, when the observation interval is less than a predetermined value, the aircraft moves at a constant speed on a straight line at a fixed height, and a state evolution model related to the state variable is established; Step 202, based on the state variable and the covariance matrix of the last moment, generating Sigma points by using Cholesky decomposition is the dimension of the state variable; Step 203, each Sigma point is substituted into the state evolution model to generate predicted points at the current time, and the weighted coefficients of the predicted points are calculated according to the sampling strategy, and the predicted state variable and the predicted covariance matrix are generated according to the weighted coefficients.

3. The method of claim 1, wherein, Step 3 specifically includes: Step 301, the sending end maps the communication data to the time delay-Doppler grid to obtain the time-frequency domain symbol, and then obtains the baseband transmission signal through the Heisenberg transform; Step 302, the sending end calculates the estimated pitch angle and azimuth angle of the receiving end according to the predicted state variable obtained in step 2, and constructs a transmission analog beam forming vector; Step 303, the sending end transmits data to the receiving end in units of frames, and the transmitted communication signal is transmitted through the line-of-sight channel, which is received by the receiving end for service transmission, and is reflected by the surface of the receiving end to form a sensing echo; Step 304, the receiver of the sending end receives and converges the echo signal by using the interleaved hybrid antenna array, and obtains a received data vector for subsequent sensing processing.

4. The method of claim 3, wherein, Step 304 specifically includes: The antenna array size of the receiver is consistent with the transmitting antenna, and an interleaved subarray structure is adopted to divide the uniform planar antenna array into subarrays, and the subarray numbers along the axis and the axis respectively, each subarray containing antenna array units; each antenna unit is connected with a phase shifter, and each subarray is connected with a radio frequency chain; the phase shifter settings of each subarray are kept consistent to obtain the receiving signal of the subarray with the index of .

5. The method of claim 1, wherein, Step 4 specifically includes: Step 401, remove the cyclic prefix of each subarray received signal and perform matched filtering, sample according to the symbol time slot and subcarrier interval, obtain the time-frequency domain symbol, and then map to the time delay-Doppler symbol domain through the symplectic Fourier transform to obtain the relationship between the received symbol and the transmitted symbol; Step 402, rearranging the received symbols of the same time slot into a matrix according to the subarray dimension, wherein each column corresponds to the received symbols of a subarray; Step 403, dividing the symbol domain received matrix according to the subarray, and performing cross-correlation operation on the adjacent subarray column vectors along the x-axis and z-axis respectively, and accumulating; Step 404, extracting the cross-correlation quantity, and calculating the estimated values of the elevation angle and the azimuth angle.

6. The method of claim 1, wherein, Step 5 specifically comprises: Step 501, constructing a digital combining vector according to the angle estimation obtained in step 4; Step 502, defining a phase correction term according to the symbol domain input-output model and the Dirichlet kernel characteristics, and performing two-dimensional cross-correlation on the received data and the transmitted symbols to form a delay-Doppler cumulative statistic; Step 503, finding the maximum value of the delay-Doppler cumulative statistic, and taking the corresponding index as the estimated values of the delay and the Doppler frequency shift.

7. The method of claim 1, wherein, Step 6 specifically comprises: Step 601, establishing an observation model according to the estimated values of the elevation angle and the azimuth angle obtained in step 4 and the estimated values of the delay and the Doppler frequency shift obtained in step 5; Step 602, based on the predicted state variable and the covariance matrix, generating Sigma points using Cholesky decomposition; Step 603, substituting each Sigma point into the observation model to generate the predicted value of the observation variable, the predicted covariance matrix, and the state-observation cross-covariance matrix; Step 604, calculating the Kalman gain, and updating the state variable and the covariance matrix at the current time.

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