Target tracking method, active radar equipment and storage medium

Abstract

The invention discloses a target tracking method, active radar equipment and a storage medium. The tracking method comprises the following steps: acquiring a radar echo signal; performing target detection on the radar echo signal, and calculating a current state parameter of a target; based on the current state parameter of the target, calculating the next state optimal prediction value of the target by using an extended Kalman filtering algorithm, and updating the state covariance matrix; and controlling the radar orientation according to the optimal predicted value of the next state of the target to realize target tracking. According to the method, the azimuth angle and the pitch angle of the target at the next moment are predicted by using the extended Kalman filtering algorithm with higher precision, and the radar orientation is controlled through the servo turntable according to the prediction result, so that the radar faces the target direction to realize tracking, the tracking performance of the high-speed radar on the high-speed target is effectively improved, and the tracking efficiency is improved. And the high-speed target tracking accuracy is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of target tracking, and in particular relates to a target tracking method, active radar equipment and a storage medium. Background Art

[0002] Although radar technology has matured, tracking high-speed targets remains a challenge. High-speed radar can track targets and predict their landing points, as well as track, monitor, and guide other coordinated targets. High-speed targets move rapidly, and conventional tracking algorithms can only track and monitor them passively, unable to predict their location in advance. Target tracking using polynomial fitting suffers from large tracking errors, making it easy to lose the target.

[0003] To ensure continuous target tracking, the radar's receptive field must be expanded, which results in radar energy dispersion and a shortened detection range. Furthermore, traditional radar ranging algorithms produce distance ambiguity for distant targets, leading to ranging failures, while dual-frequency ranging methods offer insufficient ranging accuracy.

[0004] Therefore, how to improve the tracking performance of high-speed radar for high-speed targets has become a technical problem that needs to be solved. Summary of the Invention

[0005] The object of the present invention is to provide a target tracking method, an active radar device and a storage medium to solve the problem of large tracking error in existing target tracking algorithms.

[0006] The present invention solves the above technical problems through the following technical solutions: a target tracking method, comprising:

[0007] Acquire radar echo signals;

[0008] Performing target detection on the radar echo signal and calculating current state parameters of the target;

[0009] Based on the current state parameters of the target, the extended Kalman filter algorithm is used to calculate the optimal prediction value of the next state of the target and update the state covariance matrix;

[0010] The radar direction is controlled according to the optimal predicted value of the next state of the target to achieve target tracking.

[0011] Furthermore, the extended Kalman filter algorithm is used to calculate the optimal prediction value of the next state of the target, including:

[0012] Calculating a next state prediction value of the target based on the current state parameters of the target and a state transition equation;

[0013] Calculating an uncertainty matrix based on the Jacobian matrix of the state transfer equation, the current state covariance matrix, and the process noise covariance matrix;

[0014] Calculating the Kalman gain based on the uncertainty matrix, the measurement matrix and the observation noise covariance matrix;

[0015] The optimal next state prediction value of the target is calculated based on the next state prediction value, the uncertainty matrix, the current state parameter and the Kalman gain.

[0016] Furthermore, the update formula of the state covariance matrix is: ;

[0017] in, represents the next state covariance matrix; represents the identity matrix; represents the Kalman gain; represents the observation matrix; represents the uncertainty matrix.

[0018] Furthermore, target detection is performed on the radar echo signal, including:

[0019] Pack and perform FFT on the radar echo signal of each RF channel to obtain the complex representation of the frequency domain of the data packet data;

[0020] Calculating a velocity spectrum based on a complex representation of the frequency domain of the packet data of all radio frequency channels;

[0021] Performing a peak search on the velocity spectrum using a preset velocity window to obtain a peak point;

[0022] CFAR detection is performed on the peak point to achieve target detection.

[0023] Furthermore, the current state parameters of the target include the current absolute azimuth angle, the current absolute pitch angle, the current radial velocity, the current steering angle and the current steering angular velocity of the target in the radar coordinate system.

[0024] Furthermore, the tracking method also includes dual-frequency ranging. During the dual-frequency ranging process, a fixed frequency and a second transmitting frequency together form a dual-frequency continuous wave signal. The dual-frequency ranging includes:

[0025] If the second transmission frequency corresponding to the current detection is not the last frequency and the target is detected twice in a row, the estimated target distance is calculated. Based on the estimated target distance and the quadratic frequency difference of the second transmission frequency corresponding to the two consecutive detections, whether phase ambiguity occurs is determined, and the quadratic phase difference of the second transmission frequency corresponding to the two consecutive detections is calculated.

[0026] If phase ambiguity occurs, the theoretical phase difference is calculated, and then the ambiguity number and the unambiguous phase difference are calculated. The target measurement distance is calculated based on the secondary frequency difference, primary frequency difference, unambiguous phase difference, target radial velocity and observation period of the second transmission frequency corresponding to two consecutive detections;

[0027] If no phase ambiguity occurs, the target measurement distance is calculated based on the quadratic phase difference of the second transmission frequency corresponding to two consecutive detections, the target radial velocity and the observation period.

[0028] Furthermore, the dual-frequency ranging further includes:

[0029] If the second transmission frequency corresponding to the current detection is the last frequency, the target estimated distance is calculated, and then the fuzzy number is calculated. The target measured distance is calculated based on the primary phase difference, primary frequency difference and fuzzy number of the second transmission frequency corresponding to the current detection.

[0030] Based on the same concept, the present invention further provides an active radar device, comprising a memory, a processor, and a computer program / instruction stored in the memory, wherein the processor executes the computer program / instruction to implement the target tracking method as described above.

[0031] Based on the same concept, the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon, which implements the target tracking method described above when the computer program / instruction is executed by a processor.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention uses a more accurate extended Kalman filter algorithm to predict the azimuth and pitch angles of the target at the next moment. Based on the prediction results, the radar direction is controlled by a servo turntable so that the radar is directed toward the target to achieve tracking, effectively improving the tracking performance of high-speed radar for high-speed targets and improving the accuracy of high-speed target tracking.

[0034] The present invention performs dual-frequency ranging on the basis of target state tracking, thereby enhancing the continuity of target tracking. Combining the target state and distance, the complete motion state of the target can be solved, and target tracking with high precision, low latency and strong robustness can be achieved in complex environments.

[0035] Compared with phase scanning radar, the present invention is less likely to cause false alarms and has a wide range of applications. It can be applied to target tracking of most traditional mechanical radars, has more stable tracking performance, a wide ranging range and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only one embodiment of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 is a flow chart of a target tracking method according to an embodiment of the present invention;

[0038] Figure 2 is the frequency in the embodiment of the present invention and Schematic diagram of the relationship between the primary and secondary frequency differences;

[0039] Figure 3 This is a flow chart of dual-frequency ranging in an embodiment of the present invention;

[0040] Figure 4 is the target tracking simulation result in the embodiment of the present invention;

[0041] Figure 5 1 is a comparison diagram of azimuth angle errors between the least squares prediction result and the extended Kalman filter prediction result of the present invention;

[0042] Figure 6 3 is a comparison diagram of the pitch angle error between the least squares prediction result and the extended Kalman filter prediction result of the present invention;

[0043] Figure 7 It is a moving target ranging simulation curve in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following is a clear and complete description of the technical solutions of the present invention in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts are within the scope of protection of the present invention.

[0045] The following specific embodiments are used to describe the technical solution of the present invention in detail. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0046] Example 1

[0047] In order to solve the problem of large tracking errors in existing target tracking algorithms, the present invention provides a target tracking method, which measures the speed, angle and distance of high-speed targets, and uses a more accurate extended Kalman filter algorithm to predict the azimuth and pitch angles of the target at the next moment. According to the prediction results, the radar direction is controlled by a servo turntable, and the radar is directed toward the target in advance and tracks it, effectively improving the tracking performance of the high-speed radar for high-speed targets and achieving more accurate high-speed target tracking.

[0048] like Figure 1 As shown, the target tracking method of the embodiment of the present invention includes the following steps:

[0049] Step 1: Obtain radar echo signal;

[0050] Step 2: Detect the target on the radar echo signal and calculate the current state parameters of the target;

[0051] Step 3: Based on the current state parameters of the target, the extended Kalman filter algorithm is used to calculate the optimal prediction value of the next state of the target and update the state covariance matrix;

[0052] Step 4: Control the radar direction based on the optimal prediction value of the target's next state to achieve target tracking.

[0053] The radar includes a dual-axis servo turntable for control tracking, an antenna array, a radio frequency path, an FPGA, and a signal processing board. The antenna array is square and includes multiple antennas, each of which corresponds to an radio frequency channel.

[0054] The radar of the present invention is an active radar device that simultaneously transmits two continuous wave signals of different frequencies, namely, a fixed frequency With the second transmitting frequency Together they form a dual-frequency continuous wave signal, with the second transmitting frequency The FPGA controls the switching according to the set value. The transmit beam width is controllable and includes wide, medium, and narrow beams. In this embodiment, the wide beam width is 8°, the medium beam width is 4°, and the narrow beam width is 2°. The wide beam is used for target search and has the largest receptive field. After the tracking link is established, it can be gradually switched to a narrow beam to ensure the detection range.

[0055] Assume that there are n second transmission frequencies, and the FPGA controls the second transmission frequency from Gradually switch to . Define the second transmission frequency and natural frequency The primary frequency difference for: (1)

[0056] Secondary frequency difference for: (2)

[0057] like Figure 2 As shown, each secondary frequency difference needs to satisfy the following relationship: (3) (4) … (5)

[0058] Where N is any positive integer. n second transmission frequencies are set based on the relationship that each secondary frequency difference needs to satisfy , usually n is set to 4 or 5.

[0059] In step 1, obtaining the radar echo signal specifically includes: the radar samples the signals received by multiple RF channels through ADC, down-converts, filters, etc. in sequence to obtain a baseband signal, i.e., the radar echo signal.

[0060] In a specific embodiment of the present invention, in step 2, performing target detection on the radar echo signal includes:

[0061] Step 2.1: Pack and perform FFT on the radar echo signal of each RF channel to obtain the complex representation of the data packet data in the frequency domain.

[0062] After the radar begins operating, the FPGA reads the current servo table reading (i.e., the current servo table angle, including the servo table azimuth and servo table pitch angle) according to the reading cycle (fixed frequency) and generates servo pulses at the same period as the reading cycle. The servo pulses are used to synchronize the servo table readings, ensuring the corresponding time of each servo table reading and adding a timestamp to each servo table reading. Simultaneously, the FPGA generates synchronization pulses at integer multiples of the reading cycle, packages the radar echo signal based on the synchronization pulses, and adds a timestamp to each data packet. In this embodiment, the reading cycle and the servo pulse generation period are both 5ms and are completely synchronized in time. The synchronization pulse generation period is 20ms, meaning that the servo table reading (including the servo table azimuth and servo table pitch angle) is read every 5ms, and a servo pulse is generated at the same time. A synchronization pulse is generated every 20ms.

[0063] In step 2.1, the specific implementation process of packaging and FFT of the radar echo signal of each RF channel is as follows:

[0064] For the radar echo signal of each RF channel, the radar echo signal between two adjacent synchronization pulses is packaged as a data packet to obtain multiple data packets for each RF channel; wherein, the first sampling point of each data packet is synchronized with the synchronization pulse, and the data is the first data after the second transmission frequency is switched; FFT processing is performed on the data packet of each RF channel to obtain a complex number representation of the data packet in the frequency domain.

[0065] After receiving the radar echo signals from each RF channel, the FPGA packages the radar echo signals from each RF channel according to the synchronization pulses. The packaged data packets, the synchronization pulse counts generated during the packaging process, the servo turntable readings, and the corresponding servo pulse counts are then sent to the signal processing board. The signal processing board performs FFT processing on the data packets of each RF channel to obtain the complex representation of the data packet data in the frequency domain.

[0066] Step 2.2: Calculate the velocity spectrum based on the complex representation of the frequency domain of the packet data of all RF channels, specifically including:

[0067] Perform vector summation and modulus calculation on the complex representation of the frequency domain of the corresponding data packet data of all RF channels to obtain the radar echo spectrum; based on the corresponding relationship between radar echo frequency and velocity, the radar echo spectrum is converted into a velocity spectrum.

[0068] Step 2.3: Use the preset velocity window to perform peak search on the velocity spectrum to obtain the peak point.

[0069] Step 2.4: Perform CFAR (constant false alarm rate) detection on the peak point to achieve target detection.

[0070] If the peak value of the peak point exceeds the CFAR threshold, the target is considered to be found, the cumulative number of target failures is cleared, and the current state parameters of the target are calculated, that is, the target radial velocity, absolute azimuth angle, and absolute pitch angle are calculated for target tracking. If the peak value of the peak point does not exceed the CFAR threshold, the target is considered to be not found, the cumulative number of target failures is increased by 1, the transmit beam is switched to a wide beam, and the velocity window is switched to the widest velocity window to increase the detection range, and then target detection is performed again.

[0071] If the peak values ​​of the data corresponding to multiple consecutive pulses all exceed the CFAR threshold, the radar is considered to have been able to continuously observe the target. At this time, the tracking link is established, the transmit beam is gradually switched to a narrow beam and the velocity window is the narrowest, and target detection is performed again. If the target is not found for multiple consecutive times (for example, 3 to 5 times), the target may be lost and tracking is stopped.

[0072] After the tracking link is established, the transmitting beam is gradually switched to a narrow beam and the speed window is the narrowest speed window. At the same time, the synchronization pulse period can be increased, so that more points of FFT calculation can be performed within one synchronization pulse period, thereby improving the signal-to-noise ratio and increasing the detection distance.

[0073] In step 2, the current state parameters of the target (i.e., observation results) include the current absolute azimuth angle, current absolute pitch angle, current radial velocity, current steering angle, and current steering angular velocity of the target in the radar coordinate system.

[0074] The target radial velocity can be calculated according to the peak point of the velocity spectrum. In order to obtain a more accurate target radial velocity, the center of mass method is used to calculate the target radial velocity.

[0075] The target absolute angle (including absolute azimuth and absolute pitch angle) is calculated based on the servo turntable angle (including azimuth and pitch angle) and the peak point position of the velocity spectrum. Taking the calculation of the target absolute azimuth as an example, the calculation of the target absolute azimuth based on the servo turntable azimuth and the peak point position of the velocity spectrum specifically includes:

[0076] From the complex representation of each RF channel, determine the complex data corresponding to the peak point of the velocity spectrum. Based on this complex data, calculate the phase difference between the azimuth channels. Using the phase comparison formula, calculate the relative azimuth error angle based on the phase difference between the azimuth receiving channels. Interpolate the servo turntable azimuth angle, and calculate the target absolute azimuth angle based on the interpolated result and the relative azimuth error angle. The calculation of the target absolute pitch angle is similar to the calculation of the target absolute azimuth angle.

[0077] The steering angle is the target's steering angle in the two-dimensional radar field of view. When a target is first detected, the steering angle and steering angular velocity can be set to 0. The steering angular velocity is equal to the difference between the steering angles at two adjacent moments.

[0078] Current state parameters of the target (or current state matrix) can be expressed as: ,in, Indicates the current absolute azimuth, Indicates the current absolute pitch angle, represents the current radial velocity, Indicates the current steering angle, Represents the current steering angular velocity, and the superscript T represents matrix transpose.

[0079] In a specific embodiment of the present invention, in step 3, the extended Kalman filter algorithm is used to calculate the optimal prediction value of the next state of the target, including:

[0080] Step 3.1: Calculate the next state prediction value of the target based on the current state parameters of the target and the state transition equation. The specific formula is: (6)

[0081] in, Represents the next state prediction value of the target (i.e., the state prediction matrix at the k+1th moment). All state prediction values ​​are in the radar coordinate system; Indicates the next absolute azimuth of the target; Indicates the next absolute pitch angle of the target; Indicates the next radial velocity of the target; Indicates the next turning angle of the target; Indicates the next turning angular velocity of the target; Represents the time interval between two observations (or observation period or measurement period). Each data packet corresponding to the synchronization pulse corresponds to one observation, and the time between the two observations is one observation period. Therefore, in this embodiment, is the time interval between two synchronization pulses.

[0082] Before using the extended Kalman filter algorithm for target tracking (i.e., the first tracking), the tracking parameters are initialized first, including the target state parameters, observation matrix, state covariance matrix, process noise covariance matrix and observation noise covariance matrix. The state matrix S is initialized according to the current state parameters of the detected target, and the Jacobian matrix of the state transfer equation is obtained according to the state transfer equation. :

[0083] (7)

[0084] In order to unify the dimensions, the observation matrix for: (8)

[0085] State covariance matrix for: (9)

[0086] in, Represents the state covariance coefficient, which is a configurable parameter. The state covariance matrix is ​​automatically updated during the tracking process, and the state covariance matrix reflects the uncertainty of the overall prediction.

[0087] Process noise covariance matrix for: (10)

[0088] in, Represents the process noise covariance coefficient, which is a configurable parameter. The process noise covariance matrix reflects the error of state transition, that is, the confidence of predicting the target state through state transition.

[0089] Observation noise covariance matrix for: (11)

[0090] in, The observation noise covariance matrix reflects the observation error, that is, the confidence level of the observed target state.

[0091] Step 3.2: Calculate the uncertainty matrix based on the Jacobian matrix of the state transfer equation, the current state covariance matrix, and the process noise covariance matrix. The specific formula is: (12)

[0092] in, Represents the uncertainty matrix, which represents the result of the combined effect of the uncertainty of the current state (i.e., the state covariance matrix at the current moment) and the uncertainty of the state transfer equation caused by external influences; Represents the current Jacobian matrix, which is used to update the state covariance matrix; Represents the current state covariance matrix.

[0093] Step 3.3: Calculate the Kalman gain based on the uncertainty matrix, observation matrix, and observation noise covariance matrix. The specific formula is: (13)

[0094] in, represents the Kalman gain.

[0095] Step 3.4: Calculate the optimal next state prediction value of the target based on the next state prediction value, uncertainty matrix, current state parameters and Kalman gain. The specific formula is: (14)

[0096] in, Indicates the optimal predicted value of the next state of the target.

[0097] Next state prediction value or next state prediction matrix It is obtained by linear prediction based on the state transfer equation, and there is a prediction deviation. In order to reduce the prediction deviation, it is further optimized according to formula (14) to obtain the optimal prediction value of the next state (i.e., the prediction result).

[0098] The update formula of the state covariance matrix is: (15)

[0099] in, Represents the next state covariance matrix, reflecting the uncertainty of the current prediction result; Represents the identity matrix.

[0100] In a specific embodiment of the present invention, the tracking method further includes step 5: dual-frequency ranging. Figure 3 As shown, the dual-frequency ranging process includes:

[0101] Step 5.1: Determine the second transmission frequency Is the traversal complete? If so, the second emission frequency corresponding to the current detection for (i.e. based on the second transmission frequency If the radar echo signal obtained is used for target detection), then go to step 5.2; if the traversal is not completed, then go to step 5.5.

[0102] Step 5.2: Calculate the estimated distance to the target. The specific formula is: (16)

[0103] in, Indicates the estimated distance of the target; Indicates the last target measurement distance; Indicates the target speed, with the target radial speed as the target speed; Indicates the observation period.

[0104] Step 5.3: Calculate the fuzzy number. The specific formula is: (17)

[0105] Among them, M represents a fuzzy number; floor represents rounding down; c represents the speed of light; Indicates the last second transmission frequency The primary frequency difference, .

[0106] Step 5.4: According to the second transmission frequency corresponding to the current detection The target measurement distance is calculated by the first phase difference, first frequency difference and fuzzy number. The specific formula is: (18)

[0107] in, Indicates the current target measurement distance; Indicates frequency The radar echo signal and frequency obtained at The target measurement distance is calculated after phase compensation using the fuzzy number M to ensure that there is no phase ambiguity.

[0108] Step 5.5: When a target is detected twice in succession, the estimated target distance is calculated according to formula (16).

[0109] Step 5.6: Estimate the distance to the target and the second transmission frequency corresponding to two consecutive detections and The second frequency difference Determine whether phase ambiguity occurs and calculate the quadratic phase difference of the second transmission frequency corresponding to two consecutive detections; if phase ambiguity does not occur, proceed to step 5.7; if phase ambiguity occurs, proceed to step 5.8.

[0110] In this embodiment, the secondary frequency difference The corresponding equivalent wavelength When , there will be no phase ambiguity; otherwise, there will be phase ambiguity. The calculation formula for the secondary phase difference of the second transmission frequency corresponding to two consecutive detections is: (19)

[0111] in, Indicates the second emission frequency corresponding to two consecutive detections and The secondary phase difference; Indicates the frequency of the last measurement The phase and frequency of the radar echo signal obtained at The difference between the phases of the radar echo signals obtained at , that is, the primary phase difference; Indicates the frequency of the current measurement The phase and frequency of the radar echo signal obtained at The phase difference between the radar echo signals obtained at the same time is called the first-order phase difference. The first-order phase difference can be directly obtained by conjugating and multiplying the complex data corresponding to the velocity spectrum peaks at the two frequencies and then calculating the inverse tangent.

[0112] The second emission frequency corresponding to two consecutive detections and The quadratic phase difference can be expressed as: (20) (twenty one) (twenty two)

[0113] in, Indicates frequency The phase of the radar echo signal obtained at time ; Indicates frequency The phase of the radar echo signal obtained at time ; Indicates frequency The target measurement distance under the radar echo signal obtained at the time (that is, the measured distance of the target relative to the radar); Indicates the distance the target moves during the second transmission frequency switching process; Indicates frequency The target measurement distance under the radar echo signal obtained at the same time.

[0114] Step 5.7: According to the secondary phase difference of the second transmission frequency corresponding to two consecutive detections , target radial velocity and observation period to calculate the target measurement distance. The specific formula is: (twenty three)

[0115] According to formula (22), the calculation formula (23) for the target measurement distance can be derived.

[0116] Step 5.8: Calculate the theoretical phase difference. The specific formula is: (twenty four)

[0117] in, Indicates the theoretical phase difference.

[0118] Step 5.9: Calculate the fuzzy number and the unambiguous phase difference. The specific formula is: (25) (26)

[0119] in, Indicates no ambiguous phase difference.

[0120] Step 5.10: Calculate the target measurement distance based on the secondary frequency difference, primary frequency difference, unambiguous phase difference, target radial velocity, and observation period of the second transmission frequency corresponding to two consecutive detections. The specific formula is: (27)

[0121] Figure 4 The target tracking simulation results are shown in Figure 4 It can be seen that the tracking method of the present invention is stable and insensitive to observation errors, and will not cause tracking failure due to large observation errors. Figure 5 The figure shows the azimuth error comparison between the least squares prediction result and the extended Kalman filter prediction result of the present invention. Figure 6The comparison diagram of the pitch angle error between the least squares prediction result and the extended Kalman filter prediction result of the present invention is shown in FIG. Figure 5 and Figure 6 It can be seen that the tracking azimuth error and pitch angle error of the present invention are both smaller. Figure 7 shows the moving target ranging simulation curve, Figure 7 It can be seen that the moving target distance is closer to the actual distance.

[0122] Example 2

[0123] An embodiment of the present invention further provides an active radar device, which includes: a memory, a processor, and a computer program / instruction stored in the memory, and the processor executes the computer program / instruction to implement the target tracking method in the embodiment of the present invention.

[0124] Although not shown, the active radar device includes a processor that can perform various appropriate operations and processes based on programs and / or data stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The processor can be a multi-core processor or include multiple processors. In some embodiments, the processor can include a general-purpose main processor and one or more specialized coprocessors, such as a central processing unit (CPU), a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), and so on. The RAM also stores various programs and data required for device operation. The processor, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0125] The processor and memory are used together to execute the program / instructions stored in the memory. When the program / instructions are executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0126] Although not shown, an embodiment of the present invention further provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the target tracking method in the embodiment of the present invention is implemented.

[0127] Computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0128] The above disclosure is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or modifications within the technical scope disclosed in the present invention, and they should all be covered by the scope of protection of the present invention.

Claims

1. A target tracking method, characterized in that: The tracking method includes: Acquire radar echo signals; Performing target detection on the radar echo signal and calculating current state parameters of the target; Based on the current state parameters of the target, the extended Kalman filter algorithm is used to calculate the optimal prediction value of the next state of the target and update the state covariance matrix; The radar direction is controlled according to the optimal predicted value of the next state of the target to achieve target tracking.

2. The target tracking method according to claim 1, characterized in that The extended Kalman filter algorithm is used to calculate the optimal prediction value of the next state of the target, including: Calculating a next state prediction value of the target based on the current state parameters of the target and a state transition equation; Calculating an uncertainty matrix based on the Jacobian matrix of the state transfer equation, the current state covariance matrix, and the process noise covariance matrix; Calculating the Kalman gain based on the uncertainty matrix, the measurement matrix and the observation noise covariance matrix; The optimal next state prediction value of the target is calculated based on the next state prediction value, the uncertainty matrix, the current state parameter and the Kalman gain.

3. The target tracking method according to claim 2, characterized in that The updating formula of the state covariance matrix is: ； in, represents the next state covariance matrix; represents the identity matrix; represents the Kalman gain; represents the observation matrix; represents the uncertainty matrix.

4. The target tracking method according to claim 1, wherein: Performing target detection on the radar echo signal, comprising: Pack and perform FFT on the radar echo signal of each RF channel to obtain the complex representation of the frequency domain of the data packet data; Calculating a velocity spectrum based on a complex representation of the frequency domain of the packet data of all radio frequency channels; Performing a peak search on the velocity spectrum using a preset velocity window to obtain a peak point; CFAR detection is performed on the peak point to achieve target detection.

5. The target tracking method according to claim 1, wherein: The current state parameters of the target include the current absolute azimuth, current absolute pitch angle, current radial velocity, current steering angle, and current steering angular velocity of the target in the radar coordinate system.

6. The target tracking method according to any one of claims 1 to 5, characterized in that: The tracking method also includes dual-frequency ranging. During the dual-frequency ranging process, a fixed frequency and a second transmitting frequency together form a dual-frequency continuous wave signal. The dual-frequency ranging includes: If the second transmission frequency corresponding to the current detection is not the last frequency and the target is detected twice in a row, the estimated target distance is calculated. Based on the estimated target distance and the quadratic frequency difference of the second transmission frequency corresponding to the two consecutive detections, whether phase ambiguity occurs is determined, and the quadratic phase difference of the second transmission frequency corresponding to the two consecutive detections is calculated. If phase ambiguity occurs, the theoretical phase difference is calculated, and then the ambiguity number and the unambiguous phase difference are calculated. The target measurement distance is calculated based on the secondary frequency difference, primary frequency difference, unambiguous phase difference, target radial velocity and observation period of the second transmission frequency corresponding to two consecutive detections; If no phase ambiguity occurs, the target measurement distance is calculated based on the quadratic phase difference of the second transmission frequency corresponding to two consecutive detections, the target radial velocity and the observation period.

7. The target tracking method according to claim 6, characterized in that: The dual-frequency ranging further includes: If the second transmission frequency corresponding to the current detection is the last frequency, the target estimated distance is calculated, and then the fuzzy number is calculated. The target measured distance is calculated based on the primary phase difference, primary frequency difference and fuzzy number of the second transmission frequency corresponding to the current detection.

8. An active radar device comprising a memory, a processor, and a computer program / instructions stored in the memory, characterized in that: The processor executes the computer program / instructions to implement the target tracking method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instruction is executed by a processor, the target tracking method according to any one of claims 1 to 7 is implemented.

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