Detection-before-tracking method and device for cognitive radar weak target range walk

Abstract

The application discloses a detection-before-track method for weak target distance walk of cognitive radar, and relates to the technical field of radar data processing.The method solves the problem of the prior art that the state transition step is too long, the search interval is too large, the processing efficiency is low, and the engineering practicability is not high when directly performing DP-TBD processing on the measurement data between different frames of the radar.The method comprises the following steps: converting radar echo data to a two-dimensional plane, extending the two-dimensional radar data set in the range and azimuth directions by using the maximum unambiguous distance of the radar, and obtaining an extended data set; setting a state transition step, performing first-stage DP-TBD processing on each frame of data in the extended data set, and obtaining a first-stage processing data set; performing second-stage DP-TBD processing between each frame of data in the first-stage processing data set, and obtaining a second-stage processing data set; performing track backtracking on the second-stage processing data set, and obtaining a TBD result.The method realizes high-speed target tracking under low SNR, has little influence of distance walk, and has a wide application range.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of radar data processing, and particularly relates to a detection-before-track method and device for weak target range walk of cognitive radar. BACKGROUND

[0002] The dynamic programming algorithm regards the track search problem as an optimization problem, converts the energy accumulation process of continuous multiple frames into a recursive accumulation form of multiple stages, solves the maximum energy accumulation value of each stage corresponding to the sub-track one by one, and forms an optimal track from these sub-tracks. If the corresponding energy accumulation value is greater than the detection threshold, the optimal track is determined as a target track.

[0003] The traditional radar target detection-dealiasing-target tracking process may directly remove low signal-to-noise ratio targets as noise due to the need to set a threshold for detection to screen targets, which leads to the difficulty of detecting and tracking low observable targets. The detection-before-track (DP-TBD) algorithm based on dynamic programming searches and accumulates the value function of multiple frame measurement data according to the correlation of the target between frames. The target reaches the highest value function in the last frame, while the noise does not accumulate between frames due to randomness. At this time, the threshold decision is made on the last frame data to detect the weak target. Therefore, the DP-TBD algorithm is widely used in the detection and tracking of low observable targets, and it is necessary to study the DP-TBD algorithm.

[0004] For the detection and tracking of low observable targets, some people propose to first perform the track-before-detect (TBD) technology to improve the detection performance of weak targets, and then perform the dealiasing processing to solve the range ambiguity of the target. This method effectively solves the detection and tracking of low signal-to-noise ratio targets, but still has two problems: 1) When detecting high-speed targets, the target range walk problem is prone to occur. At this time, the distance gate number of the same target under different pulse repetition frequencies cannot be corresponded after range continuation, and the dealiasing processing result will have a large error, resulting in deviation of the final output result; 2) Directly performing DP-TBD (Dynamic Programming-Track Before Detect) processing on the measurement data between different frames of the radar, the state transition step is too long, the search interval is too large, and the processing efficiency is low, which is not high in engineering practicability. SUMMARY

[0005] The application provides a pre-detection tracking method and device for weak target distance walking of cognitive radar, solves the problem that in the prior art, the measurement data between different frames of the radar is directly subjected to DP-TBD processing, the state transition step is too long, the search interval is too large, the processing efficiency is low, and the engineering practicability is not high, high-speed target tracking is realized under low SNR, the influence of distance walking is small, and the application range is wide.

[0006] In a first aspect, the application provides a pre-detection tracking method for weak target distance walking of cognitive radar, which comprises:

[0007] Converting radar echo data to a two-dimensional plane to obtain a two-dimensional radar data set;

[0008] Performing distance and azimuth extension on the two-dimensional radar data set by using the maximum unambiguous distance of the radar to obtain an extended data set;

[0009] Setting a state transition step, performing one-level DP-TBD processing on each frame of data in the extended data set to obtain a one-level processed data set;

[0010] Performing two-level DP-TBD processing between each frame of data in the one-level processed data set to obtain a two-level processed data set;

[0011] Performing track backtracking on the data set that is not 0 in the two-level processed data set to obtain a TBD result.

[0012] In a possible implementation manner of the first aspect, the performing distance and azimuth extension on the two-dimensional radar data set by using the maximum unambiguous distance of the radar to obtain an extended data set comprises:

[0013] Performing distance extension on all measurement points obtained by each PRF in each frame of the two-dimensional radar data set, and the specific extension formula is represented as:

[0014] ;

[0015] Wherein, represents distance information of the radar after distance extension on the PRF data in the i th frame; represents the i th frame; represents the i th frame; represents the i th frame; represents the i th frame; represents the i th frame; represents the number of distance extension times; represents the farthest unambiguous distance corresponding to the i th frame; represents the i th frame; represents the i th frame; represents the amplitude information of the target; Indicates the radar's maximum detection range; Indicates the first Frame number The angle corresponding to the measurement point on each repetition frequency; Used to calculate the number of distance extensions; Represents the repetition rate; Indicates the number of frames.

[0016] In conjunction with the first aspect, in one possible implementation, the setting of the state transition step size involves performing inter-repetition rate first-level DP-TBD processing on each frame of data in the extended dataset to obtain a first-level processed dataset, including:

[0017] The extended dataset is converted into a measurement matrix in the xy plane, and the first value function accumulation value of each frame is calculated respectively;

[0018] Using the preset first threshold value To each The first value function accumulation value of the frame data Make a judgment, if Then the corresponding The first frame of data The measurement matrix corresponding to each repetition frequency data point is set to 0; if Then the corresponding frame number The measurement matrix corresponding to each repetition frequency data remains unchanged;

[0019] Track backtracking is performed on the measurement matrix that is not zero to obtain the first-level processing dataset.

[0020] In conjunction with the first aspect, in one possible implementation, the accumulated value of the first value function is expressed as:

[0021] ;

[0022] in, Indicates the first Frame number The apparent distance gate corresponding to the measurement point on each repetition frequency; Indicates the first Frame number The measurement matrix of each repetition frequency; Indicates the first Frame number The angle corresponding to the measurement point on each repetition frequency; Indicates the magnitude information of the target; Indicates the first Frame number The accumulated value of the function on each repetition frequency; Indicates the first Frame number The state corresponding to the maximum value function on each repetition frequency.

[0023] With reference to the first aspect, in a possible implementation manner, the performing secondary DP-TBD processing between each frame of data in the primary processing data set to obtain a secondary processing data set comprises:

[0024] calculating a second value function cumulative value of each frame of data in the primary processing data set;

[0025] using a preset second threshold value respectively for the second value function cumulative value of each frame of data judging, if , a measurement matrix corresponding to a last frame of each frame of data is set to 0; if , the measurement matrix corresponding to the last frame of each frame of data is unchanged, to obtain the secondary processing data set.

[0026] With reference to the first aspect, in a possible implementation manner, the second value function cumulative value is expressed as:

[0027]

[0028] wherein, denotes a maximum value function corresponding to an n-th frame; denotes a measurement matrix of an n-th frame; denotes a secondary DP-TBD result of an n-th frame; denotes an angle of an n-th frame target; denotes an amplitude of an n-th frame target; denotes a maximum value function corresponding to an n-th frame; denotes a state corresponding to the maximum value function of an n-th frame.

[0029] The second aspect of the present application provides a detection-before-tracking device under weak target range migration of a cognitive radar, which comprises:

[0030] a data conversion module, configured to convert radar echo data to a two-dimensional plane to obtain a two-dimensional radar data set;

[0031] a data extension module, configured to extend the two-dimensional radar data set in range and azimuth using a radar maximum unambiguous range to obtain an extended data set;

[0032] a primary DP-TBD processing module, configured to set a state transition step, and perform primary DP-TBD processing between each frame of data in the extended data set to obtain a primary processing data set; ​​​​​​​

[0033] a second-level DP-TBD processing module, configured to perform second-level DP-TBD processing between each frame of data in the first-level processing data set, to obtain a second-level processing data set;

[0034] an output module, configured to perform track back on the data set in the second-level processing data set that is not 0, to obtain a TBD result, and output the TBD result.

[0035] In a third aspect, the present application provides a detection-before-track server for airborne cognitive radar to reduce target false alarm probability, which comprises a memory and a processor;

[0036] The memory is configured to store computer executable instructions;

[0037] The processor is configured to execute the computer executable instructions to implement the detection-before-track method for weak target distance walk of cognitive radar.

[0038] In a fourth aspect, the present application provides a computer readable storage medium, which has executable instructions, and a computer executing the executable instructions can implement the detection-before-track method for weak target distance walk of cognitive radar.

[0039] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0040] (1) The first-level TBD processing in the present application is inter-repetition TBD processing, and since the displacement of the target in one CPI is short, the state transition step is small, the search interval is small, and the processing speed is improved;

[0041] (2) The second-level DP-TBD processing in the present application solves the distance walk problem by the search characteristics of the processing range, and the obtained target track is more accurate;

[0042] (3) The present application can realize the detection and tracking of high-speed motion of multiple targets under low SNR, and has very wide application range. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 The detection-before-track method for weak target distance walk of cognitive radar provided for the embodiment of the present application is shown in the flow chart;

[0044] Figure 2 The TBD processing result after the de-masking is shown in the figure;

[0045] Figure 3 The result after the second-level DP-TBD processing is shown in the figure;

[0046] Figure 4The graph shows the comparison results of the detection probabilities of the two algorithms in 100 Monte Carlo experiments provided in this embodiment of the invention.

[0047] Figure 5 A graph showing the comparison of the root mean square error of the two algorithms provided in 100 Monte Carlo experiments in this embodiment of the invention.

[0048] Figure 6 The graph shows the comparison of the running time of the two algorithms in 100 Monte Carlo experiments provided in this embodiment of the invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0050] This invention provides a method for pre-detection tracking of weak targets at close range using cognitive radar, such as... Figure 1 As shown, the method includes the following steps S101 to S105.

[0051] S101 converts the radar echo data onto a two-dimensional plane to obtain a two-dimensional radar dataset.

[0052] For example, assume that the radar is continuously detecting Frame time, transmission at the same wavelength in each frame A pulse signal with a repetition frequency of 1, the repetition frequencies being as follows: The furthest detection distance is The radar receiver bandwidth is The maximum unambiguous distance gate at each repetition frequency is ,make For the first Frame number The range gate corresponding to the target range received by the radar at each repetition frequency. The angle between the target and the radar beam and Acquire fuzzy measurement information for distance movement. and Then, establish a two-dimensional xy plane. Its x-direction distance from the gate is within the range of The distance from the gate in the y direction is within the range of The measurement information is then processed according to the distance gate. ,angle and amplitude information Amplitude at In the plane, i.e., the resolving unit The state at is: .

[0053] S102, using the maximum unambiguous range of the radar, the two-dimensional radar dataset is extended in range and azimuth to obtain the extended dataset.

[0054] Specifically, in step S102, the two-dimensional radar dataset is extended in range and azimuth using the radar's maximum unambiguous range to obtain the extended dataset, which includes:

[0055] Range continuation is performed on all measurement points obtained from each repetition frequency in each frame of the two-dimensional radar dataset. The specific continuation formula is expressed as follows:

[0056] ;

[0057] in, Indicates that the radar is in the frame Distance information after distance extension on each repetition frequency data; Indicates the first Frame number The apparent distance gate corresponding to the measurement point on each repetition frequency; Indicates the number of distance extensions; Indicates the first Frame number The farthest unambiguous distance corresponding to each repetition frequency; Indicates the magnitude information of the target; Indicates the radar's maximum detection range; Indicates the first Frame number The angle corresponding to the measurement point on each repetition frequency; Used to calculate the number of distance extensions; Represents the repetition frequency; Indicates the number of frames.

[0058] S103, set the state transition step size, perform inter-repetition rate first-level DP-TBD processing on each frame of data in the extended dataset, and obtain the first-level processed dataset.

[0059] For example, in step S103, the state transition step size is set, and each frame of data in the extended dataset is subjected to inter-repetition rate first-level DP-TBD processing to obtain a first-level processed dataset, including the following steps S1031 to S1033.

[0060] S1031, convert the extended dataset into a measurement matrix in the xy plane, and calculate the first-value function accumulation value for each frame. Here, The data in the extended dataset of step S102 is transformed to the xy plane. Within the measurement matrix, for the th frame value function under the first accumulation, the first value function accumulation value is represented as:

[0061] ;

[0062] wherein, represents the apparent distance gate corresponding to the measurement point on the first frame value function under the first measurement matrix under the first frame value function under the first represents the angle corresponding to the measurement point on the first frame value function under the first represents the amplitude information of the target; represents the accumulation value of the function under the first frame value function under the first represents the state corresponding to the maximum value function under the first frame value function under the first

[0063] S1032, using a preset first threshold value respectively, the first value function accumulation value of the frame data is judged, if , the measurement matrix corresponding to the first repetition data of the frame data is set to 0; if , the measurement matrix corresponding to the first repetition data on the frame is unchanged. Here, after the value function accumulation, the measurement matrix under the first repetition is threshold detected:

[0064]

[0065] wherein, is the first threshold value, the purpose is to screen out the final position of the target accumulated by the value function for CPI (coherent processing interval) time.

[0066] S1033, track back the measurement matrix which is not 0 to obtain a first-level processing data set. Here, track back all the measurement matrices which are not 0, that is, the TBD result of the first frame is obtained. Then, the TBD result of the first frame ​​​Information such as the distance and amplitude of the inner target is averaged and then reassigned. In the plane.

[0067] The first-level TBD processing of this invention is inter-repetition frequency TBD processing. Since the displacement of the target within one CPI is short, the state transition step size is small and the search interval is small, which improves the processing speed.

[0068] S104. Perform secondary DP-TBD processing between each frame of data in the primary processing dataset to obtain the secondary processing dataset.

[0069] In step S104, secondary DP-TBD processing is performed between each frame of data in the primary processing dataset to obtain the secondary processing dataset, including the following steps S1041 to S1043.

[0070] S1041, calculate the cumulative value of the second-value function for each frame of the first-level processing dataset. Here, the cumulative value of the second-value function is expressed as:

[0071] ;

[0072] in, Indicates the first The maximum value function corresponding to the frame; Represents the measurement matrix of the nth frame; Indicates the first The second-level DP-TBD results of the frame; Indicates the first The angle of the frame target; Indicates the first The amplitude of the frame target; Indicates the first The maximum value function corresponding to the frame; Indicates the first The state corresponding to the frame maximum value function.

[0073] S1042, using a preset second threshold value Accumulated values ​​of the second-value function for each frame of data Make a judgment, if Then the measurement matrix corresponding to the last frame of each data frame is set to 0; if Then, the measurement matrix corresponding to the last frame of each frame remains unchanged, resulting in the secondary processing dataset. Here, after value function accumulation, the data for the second-order data is processed... Frame measurement matrix Perform threshold detection:

[0074]

[0075] in, The second threshold value is used to screen the value function accumulation The final position where the target of the frame time is located.

[0076] The application solves the distance walking problem by using the characteristics of two-stage DP-TBD processing range search, and the obtained target track is more accurate.

[0077] S105, track backtracking is performed on the data set in the two-stage processing data set which is not 0, and the TBD result is obtained. Track backtracking is performed on the data set in the two-stage processing data set which is not 0, and the TBD result is obtained. The TBD result of the frame.

[0078] The effect of the application is further illustrated by the following simulation experiment:

[0079] Simulation 1, target detection and tracking track of traditional target detection-distance ambiguity-TBD processing.

[0080] It is assumed that the airborne PD radar adopts a four-repetition frequency working mode, the repetition frequencies are 9033Hz, 9354Hz, 9603Hz and 9891Hz, the bandwidth is 7.5MHz, the maximum detection distance is 50km, the CPI is 0.03s, and the distance resolution is 20m. There are two targets in the detection area, the signal-to-noise ratios are , the initial distances are 18km and 40km respectively, the radial velocities are and , wherein the negative sign of the velocity indicates that it is away from the radar, and the positive sign indicates that it is approaching the radar, the angles with the radar are and . The crossing distances of the targets under the four repetition frequencies are all 27m, which exceeds the radar distance resolution, and the distance walking phenomenon occurs. The radar detects for 100s, i.e. 10 frames, and the simulation results are shown in Figure 2 . It can be seen from Figure 2 that the method of traditional target detection-distance ambiguity-TBD processing will cause low-observable target detection missing when distance walking occurs, and the estimated track deviates greatly from the correct track.

[0081] Simulation 2, detection track result after processing by the application.

[0082] The parameter settings in this simulation are the same as those in simulation 1, and two-stage TBD processing between repetition frequencies and frames is realized during simulation, and the simulation results are shown in Figure 3 . It can be seen from Figure 3 that not only the missing detection caused by high-speed target distance walking is avoided, but also the detection and tracking of two weak targets are successfully realized. This simulation experiment proves that the application can realize the detection and tracking of multiple high-speed weak targets under low SNR.

[0083] Simulation 3, the TBD first and then the deambiguity processing method is compared with the method of the application.

[0084] The parameter settings in the simulation are the same as those in simulation 1, the TBD first and then the range ambiguity resolution method and the method of the application are respectively used for detecting and tracking the high-speed weak target, and the processing time is recorded. In order to reduce the random influence, 100 Monte Carlo experiments are performed, the detection probability comparison result is shown in Figure 4 , the root mean square error comparison result is shown in Figure 5 , and the time ratio comparison result is shown in Figure 6 . From Figure 4 and Figure 5 it can be seen that the TBD first and then the range ambiguity resolution method has lower average detection probability and higher average root mean square error than the application when processing the high-speed weak target, so the detection and tracking performance of the application is better. From Figure 6 it can be seen that the application greatly shortens the processing time compared with the TBD first and then the range ambiguity resolution method, so the application has better engineering application ability. The application can realize the detection and tracking of high-speed motion of multiple targets under low SNR, and has very wide application range.

[0085] The application provides a detection and tracking before tracking device for weak target range movement of a cognitive radar, which comprises a data conversion module, a data extension module, a first DP-TBD processing module, a second DP-TBD processing module and an output module.

[0086] The data conversion module is used for converting radar echo data to a two-dimensional plane to obtain a two-dimensional radar data set.

[0087] The data extension module is used for extending the two-dimensional radar data set in the range and azimuth directions by using the maximum unambiguous range of the radar to obtain an extended data set.

[0088] The first DP-TBD processing module is used for setting a state transition step, performing first-level DP-TBD processing between each frame of data in the extended data set, and obtaining a first-level processed data set.

[0089] The second DP-TBD processing module is used for performing second-level DP-TBD processing between each frame of data in the first-level processed data set to obtain a second-level processed data set.

[0090] The output module is used for performing track backtracking on the data set that is not 0 in the second-level processed data set to obtain a TBD result and outputting the TBD result.

[0091] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or less operation steps can be included based on routine or non-creative labor. The order of steps listed in the embodiments is only one of the many execution orders of the steps, and does not represent the only execution order. In actual device or client product execution, the method order as shown in the embodiments or the drawings can be executed in sequence or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0092] The devices or modules illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described as various modules with functions respectively. In the implementation of the present application, the functions of the modules can be implemented in the same or multiple software and / or hardware. Of course, the modules implementing certain functions can also be implemented by multiple sub-modules or sub-units.

[0093] The methods, devices or modules described in the present application can be implemented in a computer readable program code manner, and the controller can be implemented in any appropriate manner, for example, the controller can take the form of, for example, a microprocessor or a processor, and a computer readable medium storing computer readable program code (for example, software or firmware) executable by the (micro) processor, logic gates, switches, application specific integrated circuits (Application Specific Integrated Circuit; ASIC), programmable logic controllers and embedded microcontrollers. Examples of the controller include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a pure computer readable program code manner, the same function can also be implemented by logically programming the method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers. Therefore, such a controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the devices for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0094] Some of the modules in the apparatus described herein can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such activities can depend, for example, on the implementation of the computer-implemented methods. The various activities can be carried out in a distributed computing environment, in which tasks are performed by a remote processing device, which is connected to either a local or a remote computer through a communication network.

[0095] The present application provides an airborne cognitive radar pre-track server for reducing target false alarm probability, comprising a memory and a processor; the memory is used for storing computer executable instructions; the processor is used for executing the computer executable instructions to realize a pre-track method for weak target distance walk of the cognitive radar.

[0096] The present application provides a computer readable storage medium, which has executable instructions, and the computer executes the executable instructions to realize a pre-track method for weak target distance walk of the cognitive radar.

[0097] The storage medium includes but is not limited to random access memory (English: Random Access Memory; abbreviation: RAM), read-only memory (English: Read-Only Memory; abbreviation: ROM), cache (English: Cache), hard disk (English: Hard Disk Drive; abbreviation: HDD) or memory card (English: Memory Card). The memory can be used to store computer program instructions.

[0098] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary hardware. Based on such understanding, the technical solutions of the present application can be embodied in the form of software products, or can be embodied in the form of data migration during implementation. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0099] The various embodiments in this specification are described in a progressive manner, and the same or similar parts among the various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. The whole or part of the present application can be used in a variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, mobile communication terminals, multi-processor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

[0100] The above examples are only used to illustrate the technical solutions of the present application, and are not limited to the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can still be modified, or part or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the present application.

Claims

1. A method for pre-detection tracking of weak targets at a moving distance using cognitive radar, characterized in that, include: The radar echo data is converted onto a two-dimensional plane to obtain a two-dimensional radar dataset; Using the radar's maximum unambiguous range, the two-dimensional radar dataset is extended in range and azimuth to obtain the extended dataset; Set the state transition step size, perform inter-repetition rate first-level DP-TBD processing on each frame of data in the extended dataset, and obtain the first-level processed dataset. The state transition step size is set, and inter-repetition rate first-level DP-TBD processing is performed on each frame of data in the extended dataset to obtain a first-level processed dataset, including: The extended dataset is converted into a measurement matrix in the xy plane, and the first value function accumulation value of each frame is calculated respectively; Using the preset first threshold value To each The first value function accumulation value of the frame data Make a judgment, if Then the corresponding The first frame of data The measurement matrix corresponding to each repetition frequency data point is set to 0; if Then the corresponding frame number The measurement matrix corresponding to each repetition frequency data remains unchanged; The measurement matrix that is not zero is backtracked to obtain the first-level processing dataset; Secondary DP-TBD processing is performed between each frame of data in the primary processing dataset to obtain the secondary processing dataset; The non-zero values ​​in the secondary processing dataset are traced back to obtain the TBD results.

2. The method for pre-detection tracking of weak targets at close range using cognitive radar according to claim 1, characterized in that, The method involves using the maximum unambiguous range of the radar to extend the two-dimensional radar dataset in terms of range and azimuth, resulting in an extended dataset, which includes: Range extension is performed on all measurement points obtained from each repetition frequency in each frame of the two-dimensional radar dataset. The specific extension formula is expressed as follows: ； in, Indicates that the radar is in the frame Distance information after distance extension on each repetition frequency data; Indicates the first Frame number The apparent distance gate corresponding to the measurement point on each repetition frequency; Indicates the number of distance extensions; Indicates the first Frame number The farthest unambiguous distance corresponding to each repetition frequency; Indicates the magnitude information of the target; Indicates the radar's maximum detection range; Indicates the first Frame number The angle corresponding to the measurement point on each repetition frequency; Used to calculate the number of distance extensions; Represents the repetition frequency; Indicates the number of frames.

3. The pre-detection tracking method for weak targets at moving distances using cognitive radar according to claim 1, characterized in that, The second-level DP-TBD processing is performed between each frame of data in the first-level processing dataset to obtain the second-level processing dataset, which includes: Calculate the cumulative value of the second-value function for each frame of the first-level processing dataset; Using a preset second threshold value Accumulated values ​​of the second-value function for each frame of data Make a judgment, if Then the measurement matrix corresponding to the last frame of each data frame is set to 0; if If the measurement matrix corresponding to the last frame of each data frame remains unchanged, then the secondary processing dataset is obtained.

4. A pre-detection tracking device for weak targets moving at close range using cognitive radar, characterized in that, include: The data conversion module is used to convert radar echo data onto a two-dimensional plane to obtain a two-dimensional radar dataset. The data extension module is used to extend the two-dimensional radar dataset in range and azimuth using the radar's maximum unambiguous range, to obtain the extended dataset. The first-level DP-TBD processing module is used to set the state transition step size and perform inter-repetition-frequency first-level DP-TBD processing on each frame of data in the extended dataset to obtain the first-level processed dataset. The state transition step size is set, and inter-repetition rate first-level DP-TBD processing is performed on each frame of data in the extended dataset to obtain a first-level processed dataset, including: The extended dataset is converted into a measurement matrix in the xy plane, and the first value function accumulation value of each frame is calculated respectively; Using the preset first threshold value To each The first value function accumulation value of the frame data Make a judgment, if Then the corresponding The first frame of data The measurement matrix corresponding to each repetition frequency data point is set to 0; if Then the corresponding frame number The measurement matrix corresponding to each repetition frequency data remains unchanged; The measurement matrix that is not zero is backtracked to obtain the first-level processing dataset; The secondary DP-TBD processing module is used to perform secondary DP-TBD processing between each frame of data in the primary processing dataset to obtain the secondary processing dataset. The output module is used to perform track backtracking on the non-zero data sets in the secondary processing dataset to obtain TBD results and output them.

5. A pre-detection tracking server for airborne cognitive radar to reduce the probability of missed detection of targets, characterized in that, Including memory and processor; The memory is used to store computer-executable instructions; The processor is used to execute the computer-executable instructions to implement the cognitive radar pre-detection tracking method for weak target distance movement as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium has executable instructions, and when the computer executes the executable instructions, it can implement the pre-detection tracking method for weak targets at a distance as described in any one of claims 1-3.

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