Anti-jamming tracking method and device for radar tracking system, equipment and medium

Abstract

This application provides a radar tracking system anti-interference tracking method, apparatus, equipment, and medium, relating to the field of data processing technology. In this application, firstly, the acquired radar echo signal undergoes signal-to-noise ratio (SNR) determination processing to obtain the target SNR; secondly, based on the interference level characterized by the target SNR, the process noise covariance matrix and the measurement noise covariance matrix are adjusted to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices. The process noise covariance matrix is ​​used to control the growth of the state covariance during the prediction phase, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain during the update phase; then, Kalman filtering is performed based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to obtain the target state information. Based on the above, the relatively low reliability of anti-interference tracking in existing technologies can be improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to an anti-interference tracking method, apparatus, device, and medium for a radar tracking system. Background Technology

[0002] With the widespread application of modern radar systems in complex environments, radar target tracking technology has become an important research direction. In practical applications, radar signals are often affected by interference sources such as clutter, interfering signals, and noise. These interferences severely affect the tracking accuracy and reliability of radar systems, especially in high-noise environments or under conditions of poor signal quality. Existing radar target tracking methods typically rely on traditional Kalman filtering techniques, which mainly estimate the target's state through signal processing techniques and predictive models. However, existing technologies still have certain limitations in terms of anti-jamming capabilities. In particular, when encountering strong interference, the performance of tracking algorithms degrades significantly, leading to large errors in target state estimation and thus affecting the reliability of the system. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a radar tracking system anti-jamming tracking method, apparatus, equipment and medium to improve the problem of relatively low reliability of anti-jamming tracking in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] An anti-jamming tracking method for a radar tracking system, comprising:

[0006] The acquired radar echo signal is processed to determine the signal-to-noise ratio, thereby obtaining the target signal-to-noise ratio, wherein the radar echo signal is the radar signal reflected by the target tracking object;

[0007] Based on the interference level characterized by the target signal-to-noise ratio, the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter are adjusted to form the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix. The process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction stage of the Kalman filter, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update stage of the Kalman filter.

[0008] Kalman filtering is performed based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information, wherein the target state information includes at least one of the position, velocity and heading of the target tracking object.

[0009] In a preferred embodiment of this application, in the aforementioned anti-jamming tracking method for a radar tracking system, the step of adjusting the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter based on the interference level characterized by the target signal-to-noise ratio to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices includes:

[0010] Based on the relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold, the target interference level represented by the target signal-to-noise ratio is determined.

[0011] Based on the target interference level characterized by the target signal-to-noise ratio, a nonlinear mapping method is used to determine the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter, wherein the target adjustment method includes at least the adjustment coefficients;

[0012] The process noise covariance matrix and the measurement noise covariance matrix are adjusted based at least on the adjustment coefficients included in the target adjustment method to form an adjusted process noise covariance matrix and an adjusted measurement noise covariance matrix.

[0013] In a preferred embodiment of this application, in the aforementioned radar tracking system anti-jamming tracking method, the step of determining the target interference level represented by the target signal-to-noise ratio based on the relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold includes:

[0014] Determine the magnitude relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold, wherein the signal-to-noise ratio threshold includes a first threshold and a second threshold that is less than the first threshold;

[0015] When the target signal-to-noise ratio is greater than the first threshold, the target interference level represented by the target signal-to-noise ratio is determined to be the first level, wherein the first level represents that the interference is normal;

[0016] When the target signal-to-noise ratio is less than or equal to the first threshold and greater than the second threshold, the target interference level represented by the target signal-to-noise ratio is determined to be the second level, wherein the second level represents that the interference is mild;

[0017] When the target signal-to-noise ratio is less than or equal to the second threshold, the target interference level represented by the target signal-to-noise ratio is determined to be the third level, wherein the third level represents severe interference.

[0018] In a preferred embodiment of this application, in the aforementioned radar tracking system anti-jamming tracking method, the step of determining the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter based on the target interference level characterized by the target signal-to-noise ratio using a nonlinear mapping method includes:

[0019] When the target interference level represented by the target signal-to-noise ratio is the first level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter are determined as a first preset value and a second preset value, wherein the first preset value is used to adjust the process noise covariance matrix and the second preset value is used to adjust the measurement noise covariance matrix.

[0020] When the target interference level represented by the target signal-to-noise ratio is the second level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as a third preset value and a fourth preset value. The third preset value is used to adjust the process noise covariance matrix, and the fourth preset value is used to adjust the measurement noise covariance matrix. The interference level represented by the second level is higher than the interference level represented by the first level. The third preset value is greater than the first preset value, and the fourth preset value is greater than the second preset value.

[0021] When the target interference level represented by the target signal-to-noise ratio is level three, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as a fifth preset value and a sixth preset value. The fifth preset value is used to adjust the process noise covariance matrix, and the sixth preset value is used to adjust the measurement noise covariance matrix. The interference level represented by the third level is higher than the interference level represented by the second level, the fifth preset value is greater than the third preset value, and the sixth preset value is greater than the fourth preset value.

[0022] In a preferred embodiment of this application, in the aforementioned radar tracking system anti-jamming tracking method, the step of determining the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter to a fifth preset value and a sixth preset value when the target interference level represented by the target signal-to-noise ratio is the third level includes:

[0023] When the target interference level represented by the target signal-to-noise ratio is the third level, a first parameter and a second parameter are determined, wherein the first parameter is greater than the third preset value and the second parameter is greater than the fourth preset value;

[0024] The historical state information obtained from the state estimation at the previous time step is acquired, and the prediction residual is determined based on the historical state information, wherein the prediction residual is used to characterize the error between the estimated historical state information and the measured state information;

[0025] The relationship between the predicted residual and a pre-configured residual threshold is determined, and when the predicted residual is greater than the residual threshold, a third parameter is determined, wherein the third parameter is greater than 1;

[0026] A fifth preset value is determined based on the first parameter, and a sixth preset value is determined based on the second parameter and the third parameter. The fifth preset value and the sixth preset value are the adjustment coefficients included in the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter.

[0027] In a preferred embodiment of this application, in the aforementioned anti-jamming tracking method for a radar tracking system, the step of adjusting the process noise covariance matrix and the measurement noise covariance matrix based at least on the adjustment coefficients included in the target adjustment method to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices includes:

[0028] Based on the first coefficient of the adjustment included in the target adjustment method, the process noise covariance matrix is ​​weighted and adjusted to form the adjusted process noise covariance matrix;

[0029] Based on the second coefficient of the adjustment included in the target adjustment method, the measurement noise covariance matrix is ​​weighted and adjusted to form the adjusted measurement noise covariance matrix.

[0030] In a preferred embodiment of this application, the radar tracking system anti-jamming tracking method further includes:

[0031] The prediction window length is determined based on the interference level characterized by the target signal-to-noise ratio;

[0032] Based on the prediction window length, original trajectory data including at least a portion of the target state information is determined, wherein the length of the original trajectory data is equal to the prediction window length;

[0033] The original trajectory data is subjected to anomaly identification, and based on the identified anomalies, the original trajectory data is subjected to anomaly resistance processing to form anomaly resistance trajectory data.

[0034] Based on the anti-anomaly trajectory data, trajectory prediction is performed to form predicted trajectory data.

[0035] This application also provides an anti-jamming tracking device for a radar tracking system, comprising:

[0036] The signal-to-noise ratio (SNR) determination module is used to perform SNR determination processing on the acquired radar echo signal to obtain the target SNR, wherein the radar echo signal is the radar signal reflected by the target tracking object;

[0037] The covariance matrix adjustment module is used to adjust the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter based on the interference level characterized by the target signal-to-noise ratio, forming adjusted process noise covariance matrix and adjusted measurement noise covariance matrix. The process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction stage of the Kalman filter, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update stage of the Kalman filter.

[0038] The state estimation module is used to perform Kalman filtering based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information, wherein the target state information includes at least one of the position, velocity and heading of the target tracking object.

[0039] Based on the above, this application also provides an electronic device, including:

[0040] Memory, used to store computer programs;

[0041] A processor connected to the memory is used to execute the computer program stored in the memory to implement the aforementioned anti-jamming tracking method for radar tracking systems.

[0042] Based on the above, this application also provides a computer-readable storage medium storing a computer program that, when executed, performs the various steps of the radar tracking system anti-jamming tracking method described above.

[0043] The radar tracking system anti-interference tracking method, apparatus, equipment, and medium provided in this application firstly perform signal-to-noise ratio (SNR) determination processing on the acquired radar echo signal to obtain the target SNR; secondly, based on the interference level characterized by the target SNR, the process noise covariance matrix and the measurement noise covariance matrix are adjusted to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices, wherein the process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction stage, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update stage; then, Kalman filtering is performed based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to obtain the target state information. Based on the above, since the basic principle of Kalman filtering is to predict the system state at each moment and update it according to the new measurement value, the performance of Kalman filtering depends on the accurate setting of the process noise covariance matrix and the measurement noise covariance matrix. Therefore, in the solution provided in this application, the process noise covariance matrix and the measurement noise covariance matrix are adjusted based on the signal-to-noise ratio, so that the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix have higher accuracy, thereby ensuring the reliability of Kalman filtering, realizing reliable tracking of the state of the target object, and thus improving the problem of relatively low reliability of anti-interference tracking in the prior art. Attached Figure Description

[0044] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0045] Figure 1 A structural block diagram of an electronic device provided in an embodiment of this application.

[0046] Figure 2 This is a flowchart illustrating the anti-interference tracking method for a radar tracking system provided in an embodiment of this application.

[0047] Figure 3 This is a block diagram of an anti-interference tracking device for a radar tracking system provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0050] like Figure 1 As shown in the figure, this application provides an electronic device. The electronic device may include a memory, a processor, and an anti-interference tracking device for a radar tracking system.

[0051] Specifically, the memory and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, the memory and the processor can be electrically connected via one or more communication buses or signal lines. The radar tracking system anti-jamming tracking device includes at least one software functional module stored in the memory in the form of software or firmware. The processor is used to execute executable computer programs stored in the memory, such as the software functional modules and computer programs included in the radar tracking system anti-jamming tracking device, to implement the radar tracking system anti-jamming tracking method provided in the embodiments of this application.

[0052] Optionally, the memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0053] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0054] Understandable. Figure 1The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown may include, for example, a communication unit for exchanging information with other devices.

[0055] Combination Figure 2 This application also provides an anti-jamming tracking method for a radar tracking system applicable to the aforementioned electronic device. The method steps defined in the relevant process of the anti-jamming tracking method for the radar tracking system can be implemented by the electronic device. The following will describe... Figure 2 The specific process shown will be explained in detail.

[0056] Step S110: Perform signal-to-noise ratio determination processing on the acquired radar echo signal to obtain the target signal-to-noise ratio.

[0057] In this embodiment, the electronic device can perform signal-to-noise ratio (SNR) determination processing on the acquired radar echo signal to obtain the target SNR. The radar echo signal is the radar signal reflected by the target tracking object (such as a drone). It should be noted that the SNR determination processing can be performed in the following ways:

[0058] SNR = 10*log 10 (Ps / Pn);

[0059] Where SNR is the target signal-to-noise ratio, Ps is the signal power, and Pn is the noise power.

[0060] Step S120: Based on the interference level characterized by the target signal-to-noise ratio, adjust the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter to form the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix.

[0061] In this embodiment, after obtaining the target signal-to-noise ratio (SNR), the electronic device can adjust the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter based on the interference level represented by the target SNR, forming adjusted process noise covariance matrices and adjusted measurement noise covariance matrices. The process noise covariance matrix controls the growth of the state covariance during the prediction phase of the Kalman filter, and the measurement noise covariance matrix controls the magnitude of the Kalman gain during the update phase of the Kalman filter. These two matrices are adjustment parameters of the Kalman filter; by setting them appropriately, the filter can perform more stably and efficiently under different environmental and noise conditions.

[0062] Step S130: Perform Kalman filtering based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information.

[0063] In this embodiment, after obtaining the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix, the electronic device can perform Kalman filtering based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information. The target state information includes at least one of the target tracking object's position, velocity, and heading, or all of these. It should be noted that the specific process of performing Kalman filtering to achieve state estimation can refer to existing Kalman filtering techniques (for example, IMM interactive multiple modeling can be used to achieve state estimation; the specific process can refer to existing techniques, such as uniform speed model, uniform acceleration model and turning model, the posterior probability of each model can be calculated, and the state estimation results of each model can be weighted and fused, thus making the reliability higher). No specific limitations are made here. The focus of this application's embodiments is on how to adjust the process noise covariance matrix and the measurement noise covariance matrix so that they can be matched with the signal-to-noise ratio, thereby ensuring the reliability of the process noise covariance matrix and the measurement noise covariance matrix, and making the filtering effect more stable and efficient.

[0064] Based on the above, since the basic principle of Kalman filtering is to predict the system state at each moment and update it according to the new measurement value, the performance of Kalman filtering depends on the accurate setting of the process noise covariance matrix and the measurement noise covariance matrix. Therefore, in the solution provided in this application, the process noise covariance matrix and the measurement noise covariance matrix are adjusted based on the signal-to-noise ratio, so that the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix have higher accuracy, thereby ensuring the reliability of Kalman filtering, realizing reliable tracking of the state of the target object, and thus improving the problem of relatively low reliability of anti-interference tracking in the prior art.

[0065] Firstly, regarding step S120, it should be noted that there are no restrictions on how the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are adjusted, and appropriate choices can be made according to actual needs.

[0066] For example, in an alternative implementation, in order to ensure that the adjustments made to the process noise covariance matrix and the measurement noise covariance matrix are adequately matched with the signal-to-noise ratio (i.e., the actual environmental and noise conditions), the above step S120 may further include steps S121, S122 and S123, wherein the specific contents of each step are as follows.

[0067] Step S121: Based on the relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold, determine the target interference level represented by the target signal-to-noise ratio.

[0068] In this embodiment, the target interference level represented by the target signal-to-noise ratio (SNR) can be determined based on the relationship between the target SNR and a pre-configured SNR threshold. In other words, different target interference levels can be determined according to different relationships with the SNR threshold, allowing for reliable differentiation of interference levels.

[0069] Step S122: Based on the target interference level characterized by the target signal-to-noise ratio, a target adjustment method is determined by using a nonlinear mapping approach to adjust the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter.

[0070] In this embodiment, after obtaining the target interference level, a target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter can be determined based on the target interference level characterized by the target signal-to-noise ratio using a nonlinear mapping method. The target adjustment method includes at least adjustment coefficients; that is, corresponding adjustment coefficients can be obtained for different target interference levels through nonlinear mapping.

[0071] Step S123: Based at least on the adjustment coefficients included in the target adjustment method, adjust the process noise covariance matrix and the measurement noise covariance matrix to form the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix.

[0072] In this embodiment, after determining the target adjustment method, the process noise covariance matrix and the measurement noise covariance matrix can be adjusted based on the adjustment coefficients included in the target adjustment method to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices. That is, the process noise covariance matrix and the measurement noise covariance matrix can be adjusted based solely on the determined adjustment coefficients, or further adjustments can be made based on the determined adjustment coefficients, configured according to actual needs. For example, the target adjustment method may also include other adjustments.

[0073] It can be understood that in the above step S121, the specific manner of determining the target interference level characterized by the target signal-to-noise ratio is not limited. For example, in an alternative implementation, in order to reliably determine the target interference level, the above step S121 may further include step S121a, step S121b, step S121c, and step S112d, and the specific content is as follows.

[0074] Step S121a, determine the magnitude relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold.

[0075] In the embodiments of the present application, the magnitude relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold can be determined. Among them, the signal-to-noise ratio threshold includes a first threshold and a second threshold smaller than the first threshold. Exemplarily, the first threshold can be -15dB, and the second threshold can be -25dB. In other embodiments, other configurations and selections can also be made.

[0076] Step S121b, when the target signal-to-noise ratio is greater than the first threshold, determine that the target interference level characterized by the target signal-to-noise ratio is the first level.

[0077] In the embodiments of the present application, when the target signal-to-noise ratio is greater than the first threshold, determine that the target interference level characterized by the target signal-to-noise ratio is the first level. Among them, the first level indicates that the interference is normal. For example, when SNR > -15dB, it is in a normal state, and the interference level = 0 level.

[0078] Step S121c, when the target signal-to-noise ratio is less than or equal to the first threshold and greater than the second threshold, determine that the target interference level characterized by the target signal-to-noise ratio is the second level.

[0079] In the embodiments of the present application, when the target signal-to-noise ratio is less than or equal to the first threshold and greater than the second threshold, determine that the target interference level characterized by the target signal-to-noise ratio is the second level. Among them, the second level indicates that the interference is mild. For example, when -25dB < SNR ≤ -15dB, it is mild interference, and the interference level = 1 level.

[0080] Step S112d, when the target signal-to-noise ratio is less than or equal to the second threshold, determine that the target interference level characterized by the target signal-to-noise ratio is the third level.

[0081] In the embodiments of the present application, when the target signal-to-noise ratio is less than or equal to the second threshold, determine that the target interference level characterized by the target signal-to-noise ratio is the third level. Among them, the third level indicates that the interference is severe. For example, when SNR ≤ -25dB, it is severe interference, and the interference level = 2 level.

[0082] It is understood that in step S122 above, the specific method for determining the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter is not limited. For example, in an alternative implementation, in order to ensure the reliability of the determined target adjustment method, step S122 above may further include steps S122a, S122b and S122c, wherein the specific contents of each step are as follows.

[0083] Step S122a: When the target interference level represented by the target signal-to-noise ratio is the first level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as the first preset value and the second preset value.

[0084] In this embodiment of the application, when the target interference level represented by the target signal-to-noise ratio is a first level, the adjustment coefficients included in the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as a first preset value and a second preset value. The first preset value is used to adjust the process noise covariance matrix, and the second preset value is used to adjust the measurement noise covariance matrix. For example, if the first level indicates that the interference is in a normal state, both the first preset value and the second preset value can be equal to 1.

[0085] Step S122b: When the target interference level represented by the target signal-to-noise ratio is the second level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as the third preset value and the fourth preset value.

[0086] In this embodiment, when the target interference level represented by the target signal-to-noise ratio is the second level, the adjustment coefficients included in the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as a third preset value and a fourth preset value. The third preset value is used to adjust the process noise covariance matrix, and the fourth preset value is used to adjust the measurement noise covariance matrix. Since the interference level represented by the second level is higher than that represented by the first level, the third preset value is greater than the first preset value, and the fourth preset value is greater than the second preset value. For example, if the second level represents a mild interference state, both the third and fourth preset values ​​can be equal to 5.

[0087] In step S122c, when the target interference level represented by the target signal-to-noise ratio is the third level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined to be the fifth preset value and the sixth preset value.

[0088] In this embodiment, when the target interference level represented by the target signal-to-noise ratio is level three, the adjustment coefficients included in the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined to be a fifth preset value and a sixth preset value. The fifth preset value is used to adjust the process noise covariance matrix, and the sixth preset value is used to adjust the measurement noise covariance matrix. Since the interference level represented by level three is higher than that represented by level two, the fifth preset value is greater than the third preset value, and the sixth preset value is greater than the fourth preset value. For example, if level three represents severe interference, both the fifth and sixth preset values ​​can be equal to 20.

[0089] It is understood that in step S122c above, the specific method of determining the adjustment coefficients for the target adjustment method is not limited. For example, in an alternative implementation, in order to effectively adapt to the situation of severe interference, step S122c above may also include anti-wild value processing. Specifically, step S122c above may further include steps c1, c2, c3 and c4, wherein the specific contents of each step are as follows.

[0090] Step c1: When the target interference level represented by the target signal-to-noise ratio is the third level, determine the first parameter and the second parameter.

[0091] In this embodiment of the application, when the target interference level represented by the target signal-to-noise ratio is level three, a first parameter and a second parameter are determined. The first parameter is greater than a third preset value, and the second parameter is greater than a fourth preset value. For example, both the first parameter and the second parameter can be equal to 20.

[0092] Step c2: Obtain the historical state information obtained from the state estimation at the previous time step, and determine the prediction residual based on the historical state information.

[0093] In this embodiment, historical state information obtained from state estimation at the previous time step can be acquired, and a prediction residual can be determined based on the historical state information. The prediction residual characterizes the error between the estimated historical state information and the measured state information. That is, the error between the estimated value and the actual value at the previous time step can be calculated to obtain the corresponding prediction error. It should be noted that the error calculation method can be selected according to actual needs.

[0094] Step c3: Determine the magnitude relationship between the predicted residual and the pre-configured residual threshold, and when the predicted residual is greater than the residual threshold, determine a third parameter.

[0095] In this embodiment of the application, after obtaining the prediction error, the relationship between the prediction residual and a pre-configured residual threshold can be determined, and when the prediction residual is greater than the residual threshold, a third parameter is determined. The third parameter is greater than 1; for example, the value of the third parameter can be greater than or equal to 10 or less than or equal to 100.

[0096] Step c4: Determine a fifth preset value based on the first parameter, and determine a sixth preset value based on the second parameter and the third parameter.

[0097] In this embodiment, after obtaining the first parameter, the second parameter, and the third parameter, a fifth preset value can be determined based on the first parameter, and a sixth preset value can be determined based on the second parameter and the third parameter. The fifth and sixth preset values ​​serve as adjustment coefficients for the target adjustment method used to adjust the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter. For example, the fifth preset value is equal to the first parameter, and the sixth preset value is equal to the sum of the second parameter and the third parameter.

[0098] It is understood that in step S123 above, the specific method of adjusting the process noise covariance matrix and the measurement noise covariance matrix is ​​not limited. For example, in an alternative embodiment, in order to achieve effective adjustment of the noise covariance matrix, step S123 above may further include steps S123a and S123b, the specific contents of which are as follows.

[0099] Step S123a: Based on the first coefficient of the adjustment included in the target adjustment method, the process noise covariance matrix is ​​weighted and adjusted to form the adjusted process noise covariance matrix.

[0100] In an embodiment of the present application, the process noise covariance matrix can be weighted and adjusted based on the first coefficient of the adjustment included in the target adjustment method to form an adjusted process noise covariance matrix. For example, the coefficient can be multiplied by the process noise covariance matrix to obtain the adjusted process noise covariance matrix.

[0101] Step S123b: Based on the second coefficient of the adjustment included in the target adjustment method, the measurement noise covariance matrix is weighted and adjusted to form an adjusted measurement noise covariance matrix.

[0102] In an embodiment of the present application, the measurement noise covariance matrix can be weighted and adjusted based on the second coefficient of the adjustment included in the target adjustment method to form an adjusted measurement noise covariance matrix. For example, the coefficient can be multiplied by the measurement noise covariance matrix to obtain the adjusted measurement noise covariance matrix.

[0103] For example, when the interference level is level 0 (normal state, SNR > -15 dB), Q (process noise covariance matrix) adopts the default value Q0 (such as diag([0.1, 0.1, 0.01, 0.01])), and R (measurement noise covariance matrix) adopts the default value R0 (such as diag([1.0, 1.0])). When the interference level is level 1 (mild interference, -25 dB < SNR ≤ -15 dB), Q = α1 * Q0, R = β1 * R0, where α1 = 5 and β1 = 5. When the interference level is level 2 (severe interference, SNR ≤ -25 dB), Q = α2 * Q0, R = β2 * R0, where α2 = 20 and β2 = 20.

[0104] Second, it should be further noted that for the anti-interference tracking method of the radar tracking system, in an alternative embodiment, the anti-interference tracking method of the radar tracking system may further include the following steps:

[0105] First, the prediction window length can be determined based on the interference degree characterized by the target signal-to-noise ratio. For example, the higher the interference degree, the longer the prediction window length, and the lower the interference degree, the shorter the prediction window length. Among them, the prediction window length can be 20 - 50 time instants;

[0106] Second, the original trajectory data including at least part of the information in the target state information can be determined based on the prediction window length. Among them, the length of the original trajectory data is equal to the prediction window length, such as including the state information of 20 - 50 time instants;

[0107] Then, outlier identification can be performed on the original trajectory data, and based on the identified outliers, anti-anomaly processing can be performed on the original trajectory data to form anti-anomaly trajectory data. For example, the deviation between each data point (trajectory point) and the fitted curve can be calculated to determine whether it exceeds k times the standard deviation. If it exceeds k times the standard deviation, it is determined to be an outlier. Then, the outlier is removed. The value of k can be configured according to actual needs.

[0108] Finally, trajectory prediction can be performed based on the anti-anomaly trajectory data to form predicted trajectory data. For example, prediction can be made based on the changing trends of each data point in the anti-anomaly trajectory data, such as using existing technologies related to fitting curves and prediction models to achieve trajectory prediction.

[0109] Thirdly, regarding the anti-jamming tracking method of the radar tracking system, it should be further explained that, in an alternative embodiment, the anti-jamming tracking method of the radar tracking system may further include the following steps:

[0110] Step 1, State Memory: During normal tracking, the memory buffer is continuously updated to save information such as target position, speed, and heading, that is, to save the target state information obtained at each moment.

[0111] Step 2, Loss Detection: When no target echo (i.e., radar signal reflected by the target being tracked) is detected for 3-5 consecutive cycles, tracking is determined to be lost.

[0112] Step 3, Search Area Prediction: Calculate the search area radius based on the direction and magnitude of the velocity vector at the last moment: e.g., R = V max ·T search Among them, V max Take the target's maximum possible speed, T search The search time window can be dynamically adjusted based on the level of interference.

[0113] Step 4, hierarchical search: Level 1 coarse scan, Level 2 fine tracking, Level 3 confirmation.

[0114] Step 5, Tracking Recovery: Resume tracking immediately after recapturing the target.

[0115] It should be further explained that, based on the above-mentioned anti-jamming tracking method for radar tracking systems, the following can be achieved:

[0116] First, it offers fast recapture speed. The memory-guided fast recapture method uses the state information of the last moment to predict the search area, and the recapture time is ≤200ms, which is 5 to 10 times faster than the 1 to 2 seconds of traditional methods.

[0117] Second, it has strong anti-interference tracking capability. The Q / R matrix nonlinear adaptive adjustment method based on SNR detection enables the filter to adapt to environments with different interference intensities, and it can still maintain stable tracking in an environment with a signal-to-noise ratio of -10dB, with tracking accuracy improved by more than 60% compared with traditional methods.

[0118] Third, the trajectory prediction accuracy is high. The pollution-resistant trajectory prediction method can still maintain a prediction accuracy of over 80% even under interference environments.

[0119] Fourth, it is highly targeted. Designed specifically for the special scenarios of high-power anti-drone systems, it fully considers the characteristics of high interference intensity, high target mobility, and high timeliness requirements.

[0120] Fifth, it is highly practical. This method has low requirements for hardware platforms and can be implemented on existing radar systems through algorithm upgrades, without the need for large-scale hardware modifications.

[0121] Combination Figure 3 This application also provides an anti-jamming tracking device for a radar tracking system applicable to the aforementioned electronic equipment. The anti-jamming tracking device may include a signal-to-noise ratio determination module, a covariance matrix adjustment module, and a state estimation module.

[0122] The signal-to-noise ratio (SNR) determination module is used to perform SNR determination processing on the acquired radar echo signal to obtain the target SNR, wherein the radar echo signal is the radar signal reflected by the target being tracked. In this embodiment, the SNR determination module can be used to perform... Figure 2 The relevant content regarding the signal-to-noise ratio determination module in step S110 shown can be found in the previous description of step S110.

[0123] The covariance matrix adjustment module is used to adjust the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter based on the interference level represented by the target signal-to-noise ratio, forming adjusted process noise covariance matrices and adjusted measurement noise covariance matrices. The process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction phase of the Kalman filter, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update phase of the Kalman filter. In this embodiment, the covariance matrix adjustment module can be used to perform... Figure 2 The details of step S120, and the relevant content regarding the covariance matrix adjustment module, can be found in the preceding description of step S120.

[0124] The state estimation module is used to perform Kalman filtering based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information, wherein the target state information includes at least one of the position, velocity, and heading of the target tracking object. In this embodiment, the state estimation module can be used to perform... Figure 2 The relevant content regarding the state estimation module in step S130 shown can be found in the previous description of step S130.

[0125] In this embodiment of the application, corresponding to the above-described anti-interference tracking method for a radar tracking system applied to the electronic device, a computer-readable storage medium is also provided, which stores a computer program that executes the various steps of the anti-interference tracking method for the radar tracking system when the computer program is run.

[0126] The steps executed by the aforementioned computer program during runtime will not be described in detail here, but can be found in the explanation of the anti-interference tracking method of the radar tracking system described above.

[0127] In summary, the radar tracking system anti-interference tracking method, apparatus, equipment, and medium provided in this application firstly perform signal-to-noise ratio (SNR) determination processing on the acquired radar echo signal to obtain the target SNR; secondly, adjust the process noise covariance matrix and measurement noise covariance matrix based on the interference level characterized by the target SNR to form adjusted process noise covariance matrix and adjusted measurement noise covariance matrix, wherein the process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction stage, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update stage; then, perform Kalman filtering based on the adjusted process noise covariance matrix and adjusted measurement noise covariance matrix to obtain the target state information. Based on the above, since the basic principle of Kalman filtering is to predict the system state at each moment and update it according to the new measurement value, the performance of Kalman filtering depends on the accurate setting of the process noise covariance matrix and the measurement noise covariance matrix. Therefore, in the solution provided in this application, the process noise covariance matrix and the measurement noise covariance matrix are adjusted based on the signal-to-noise ratio, so that the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix have higher accuracy, thereby ensuring the reliability of Kalman filtering, realizing reliable tracking of the state of the target object, and thus improving the problem of relatively low reliability of anti-interference tracking in the prior art.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0129] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0130] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0131] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An anti-interference tracking method for a radar tracking system, characterized in that, include: The acquired radar echo signal is processed to determine the signal-to-noise ratio, thereby obtaining the target signal-to-noise ratio, wherein the radar echo signal is the radar signal reflected by the target tracking object; Based on the relationship between the target signal-to-noise ratio (SNR) and a pre-configured SNR threshold, the target interference level represented by the target SNR is determined. Based on the target interference level represented by the target SNR, a nonlinear mapping method is used to determine a target adjustment method for adjusting the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter. The target adjustment method includes at least adjustment coefficients. Based at least on the adjustment coefficients included in the target adjustment method, the process noise covariance matrix and the measurement noise covariance matrix are adjusted to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices. The process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction phase of the Kalman filter, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update phase of the Kalman filter. Kalman filtering is performed based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information, wherein the target state information includes at least one of the position, velocity and heading of the target tracking object.

2. The anti-interference tracking method for a radar tracking system according to claim 1, characterized in that, The step of determining the target interference level represented by the target signal-to-noise ratio based on the relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold includes: Determine the magnitude relationship between the target signal-to-noise ratio and a pre-configured signal-to-noise ratio threshold, wherein the signal-to-noise ratio threshold includes a first threshold and a second threshold that is less than the first threshold; When the target signal-to-noise ratio is greater than the first threshold, the target interference level represented by the target signal-to-noise ratio is determined to be the first level, wherein the first level represents that the interference is normal; When the target signal-to-noise ratio is less than or equal to the first threshold and greater than the second threshold, the target interference level represented by the target signal-to-noise ratio is determined to be the second level, wherein the second level represents that the interference is mild; When the target signal-to-noise ratio is less than or equal to the second threshold, the target interference level represented by the target signal-to-noise ratio is determined to be the third level, wherein the third level represents severe interference.

3. The anti-interference tracking method for a radar tracking system according to claim 1, characterized in that, The step of determining the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter based on the target interference level characterized by the target signal-to-noise ratio, using a nonlinear mapping approach, includes: When the target interference level represented by the target signal-to-noise ratio is the first level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter are determined as a first preset value and a second preset value, wherein the first preset value is used to adjust the process noise covariance matrix and the second preset value is used to adjust the measurement noise covariance matrix. When the target interference level represented by the target signal-to-noise ratio is the second level, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as a third preset value and a fourth preset value. The third preset value is used to adjust the process noise covariance matrix, and the fourth preset value is used to adjust the measurement noise covariance matrix. The interference level represented by the second level is higher than the interference level represented by the first level. The third preset value is greater than the first preset value, and the fourth preset value is greater than the second preset value. When the target interference level represented by the target signal-to-noise ratio is level three, the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter are determined as a fifth preset value and a sixth preset value. The fifth preset value is used to adjust the process noise covariance matrix, and the sixth preset value is used to adjust the measurement noise covariance matrix. The interference level represented by the third level is higher than the interference level represented by the second level, the fifth preset value is greater than the third preset value, and the sixth preset value is greater than the fourth preset value.

4. The anti-interference tracking method for a radar tracking system according to claim 3, characterized in that, The step of determining the adjustment coefficients of the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter to a fifth preset value and a sixth preset value when the target interference level represented by the target signal-to-noise ratio is the third level includes: When the target interference level represented by the target signal-to-noise ratio is the third level, a first parameter and a second parameter are determined, wherein the first parameter is greater than the third preset value and the second parameter is greater than the fourth preset value; The historical state information obtained from the state estimation at the previous time step is acquired, and the prediction residual is determined based on the historical state information, wherein the prediction residual is used to characterize the error between the estimated historical state information and the measured state information; The relationship between the predicted residual and a pre-configured residual threshold is determined, and when the predicted residual is greater than the residual threshold, a third parameter is determined, wherein the third parameter is greater than 1; A fifth preset value is determined based on the first parameter, and a sixth preset value is determined based on the second parameter and the third parameter. The fifth preset value and the sixth preset value are the adjustment coefficients included in the target adjustment method for adjusting the process noise covariance matrix and measurement noise covariance matrix of the Kalman filter.

5. The anti-interference tracking method for a radar tracking system according to claim 1, characterized in that, The step of adjusting the process noise covariance matrix and the measurement noise covariance matrix based at least on the adjustment coefficients included in the target adjustment method to form adjusted process noise covariance matrices and adjusted measurement noise covariance matrices includes: Based on the first coefficient of the adjustment included in the target adjustment method, the process noise covariance matrix is ​​weighted and adjusted to form the adjusted process noise covariance matrix; Based on the second coefficient of the adjustment included in the target adjustment method, the measurement noise covariance matrix is ​​weighted and adjusted to form the adjusted measurement noise covariance matrix.

6. The anti-interference tracking method for a radar tracking system according to any one of claims 1-5, characterized in that, The anti-jamming tracking method of the radar tracking system also includes: The prediction window length is determined based on the interference level characterized by the target signal-to-noise ratio; Based on the prediction window length, original trajectory data including at least a portion of the target state information is determined, wherein the length of the original trajectory data is equal to the prediction window length; The original trajectory data is subjected to anomaly identification, and based on the identified anomalies, the original trajectory data is subjected to anomaly resistance processing to form anomaly resistance trajectory data. Based on the anti-anomaly trajectory data, trajectory prediction is performed to form predicted trajectory data.

7. An anti-interference tracking device for a radar tracking system, characterized in that, include: The signal-to-noise ratio (SNR) determination module is used to perform SNR determination processing on the acquired radar echo signal to obtain the target SNR, wherein the radar echo signal is the radar signal reflected by the target tracking object; The covariance matrix adjustment module is used to determine the target interference level represented by the target signal-to-noise ratio (SNR) based on the relationship between the target SNR and a pre-configured SNR threshold; based on the target interference level represented by the target SNR, a nonlinear mapping method is used to determine a target adjustment method for adjusting the process noise covariance matrix and the measurement noise covariance matrix of the Kalman filter, wherein the target adjustment method includes at least adjustment coefficients; based at least on the adjustment coefficients included in the target adjustment method, the process noise covariance matrix and the measurement noise covariance matrix are adjusted to form an adjusted process noise covariance matrix and an adjusted measurement noise covariance matrix, wherein the process noise covariance matrix is ​​used to control the growth of the state covariance in the prediction phase of the Kalman filter, and the measurement noise covariance matrix is ​​used to control the magnitude of the Kalman gain in the update phase of the Kalman filter; The state estimation module is used to perform Kalman filtering based on the adjusted process noise covariance matrix and the adjusted measurement noise covariance matrix to achieve state estimation and obtain target state information, wherein the target state information includes at least one of the position, velocity and heading of the target tracking object.

8. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor connected to the memory is used to execute the computer program stored in the memory to implement the anti-jamming tracking method of the radar tracking system according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a computer program that, when executed, performs the anti-jamming tracking method of the radar tracking system according to any one of claims 1-6.

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