Pre-tracking method and device for reducing target missing alarm probability of airborne cognitive radar

Abstract

The application discloses a detection-before-track method for reducing target missing alarm probability of airborne cognitive radar, relates to the technical field of radar, and solves the problem that weak high-speed targets are prone to missing alarm under the influence of ground clutter in the prior art.The method comprises the following steps: performing data processing on echo data of a target received by an airborne radar, performing space-time adaptive processing on the processed data, performing constant false alarm detection, and obtaining a target data set; performing distance extension in the distance direction on each frame of data in the target data set by using a maximum non-ambiguous distance of the radar, and obtaining an extended data set; performing one-stage DP-TBD processing between M pulse repetition frequency data in each frame of data in the extended data set, and performing two-stage DP-TBD processing between the frames of data, and obtaining a two-stage processed data set; and performing completion on the two-stage data set, and obtaining a target track; and the method realizes detection and tracking of multiple weak high-speed targets under the influence of ground clutter, and has a wide application range.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of radar technology, and particularly relates to a detection-before-track method and device for reducing target missing alarm probability of an airborne cognitive radar. BACKGROUND

[0002] In actual airborne application, the main process of signal and data processing of airborne cognitive radar includes signal preprocessing, clutter suppression, low threshold detection, de-blinking processing and target tracking. Airborne radar clutter suppression can adopt space-time adaptive method to suppress clutter in space and time domain, wherein full-dimensional STAP has the optimal performance. However, its higher calculation complexity and more required samples result in that it cannot be applied to engineering practice, and reduced-dimension STAP discards part of system degrees of freedom to replace lower calculation amount, so that the practicability is greatly improved; target tracking can adopt detection-before-track (DP-TBD) algorithm based on dynamic programming, takes 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, and solves the energy accumulation maximum value of each stage corresponding to a sub-track, and a most optimal track is formed by these sub-tracks, if the corresponding energy accumulation value is greater than a detection threshold, then the most optimal track is judged as a target track. The DP-TBD algorithm searches and accumulates value functions according to the correlation of the target between frames, and the target reaches the highest value function in the last frame, while the noise does not accumulate between frames due to randomness, so that the last frame data is threshold judged to detect the weak target.

[0003] Missing alarm refers to that the radar detection result is judged as no target when there is a target. The effect of radar detection depends not only on the algorithm performance of target tracking, but also on the information transmitted after constant false alarm (CFAR) detection. When the CFAR detection causes a weak target to be missed in a frame due to complex background, the measurement point processed by the target tracking lacks the target information of the frame, and if the conventional detection-before-track (TBD) processing is directly performed, the target value function cannot be accumulated, which causes the airborne radar to miss alarm. Therefore, it is necessary to study the airborne radar to reduce the target missing alarm probability.

[0004] For the problem of missing alarm of a weak target, the state transition step can be increased in the DP-TBD processing process. This method can reduce the target missing alarm problem caused by CFAR missing detection, but when detecting a high-speed target, the state transition step needs to be greater than the displacement of three frames of the target, so the greater the target speed is, the longer the state transition step of this method is, the larger the search interval is, and the greater the calculation amount is, so that the engineering practicability is not high. SUMMARY

[0005] This invention provides a pre-detection tracking method and device for reducing the probability of missed detection by airborne cognitive radar. It solves the problem in the prior art that airborne cognitive radar is prone to missing detection of weak high-speed targets under the influence of ground clutter. It realizes the complete detection and tracking of multiple weak high-speed targets under the influence of ground clutter and has a wide range of applications.

[0006] In a first aspect, the present invention provides a pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar, the method comprising:

[0007] Data processing is performed on the target echo data received by the airborne radar, and the processed data undergoes space-time adaptive processing to obtain a first dataset; wherein, the first dataset includes: Frame data, each frame includes Each repetition frequency data point includes: the number of Doppler channels, the number of distance gates, and the amplitude value; and All are greater than 0;

[0008] Perform constant false alarm rate (CFAR) detection on the first dataset to obtain the target dataset;

[0009] The range extension in the range direction of each frame of data in the target dataset is performed using the maximum unambiguous range of the radar to obtain the extended dataset;

[0010] For each frame of data in the extended dataset Each repetition frequency data point is subjected to first-level DP-TBD processing between repetition frequency data points to obtain a first-level processed dataset.

[0011] Using a set window length, perform secondary DP-TBD processing on the data between each frame in the primary processing dataset to obtain the secondary processing dataset;

[0012] By comparing the data in the secondary processing dataset corresponding to adjacent windows, the secondary dataset is completed to obtain the target trajectory.

[0013] In conjunction with the first aspect, in one possible implementation, the data processing of the target echo data received by the airborne radar, and the spatiotemporal adaptive processing of the processed data to obtain a first dataset, includes:

[0014] The echo data is processed using the velocity information of the target to obtain processed data;

[0015] The processed data is subjected to spatiotemporal filtering, spatiotemporal estimation, and adaptive filtering to obtain adaptive data.

[0016] The adaptive data is subjected to clutter suppression using a cross-extension factorization method to obtain the first dataset.

[0017] With reference to the first aspect, in a possible implementation manner, the constant false alarm rate detection on the first data set to obtain a target data set comprises:

[0018] converting the first data set to a plane to obtain a converted data set; wherein, represents a measurement matrix containing a range gate, a number of Doppler channels and amplitude information;

[0019] determining a to-be-detected data point in the converted data set and a reference data point corresponding to the to-be-detected data point, and calculating a clutter noise power around the target according to an average value of the to-be-detected data point and the reference data point; wherein, the reference data point is a plurality of data points adjacent to the to-be-detected data point, and the to-be-detected data point is any one data point in the converted data set;

[0020] determining a threshold factor corresponding to the to-be-detected data point, and obtaining a screening threshold in combination with the clutter noise power and the threshold factor;

[0021] screening data in the first data set by using the screening threshold and the clutter noise power to obtain a target data set.

[0022] With reference to the first aspect, in a possible implementation manner, the distance extension in the distance direction on each frame of data in the target data set by using a maximum unambiguous range of a radar to obtain an extended data set comprises:

[0023] extending the distance of each frame of data in the target data set by using a distance extension formula, wherein the distance extension formula is represented as:

[0024] ;

[0025] wherein, represents distance information after distance extension on the i th range frequency data of the j th frame of the radar; represents distance information after distance extension on the i th range frequency data of the j th frame of the radar; ; represents a range gate corresponding to a measurement point on the i th range frequency of the j th frame; represents a range gate corresponding to a measurement point on the i th range frequency of the j th frame; ; represents a distance extension number; represents a farthest unambiguous range corresponding to the i th range frequency of the j th frame; represents a farthest unambiguous range corresponding to the i th range frequency of the j th frame; ; represents amplitude information of the target; represents a farthest detection range of the radar; used to calculate the distance extension number; represents a range frequency number; The frame number is represented.

[0026] With reference to the first aspect, in a possible implementation manner, the one-level DP-TBD processing on the plurality of pulse repetition frequency data in each frame data in the extended data set comprises:

[0027] The extended data set is converted into a measurement matrix, and a first value function accumulation value of each frame is calculated respectively;

[0028] A preset first threshold value is used to judge the first value function accumulation value of each frame data, if the first value function accumulation value of the frame data is greater than the first threshold value, a measurement matrix corresponding to the first pulse repetition frequency data of the corresponding frame is set to 0; if the first value function accumulation value of the frame data is less than the first threshold value, the measurement matrix corresponding to the first pulse repetition frequency data of the corresponding frame is unchanged;

[0029] The measurement matrix that is not 0 is subjected to track backtracking to obtain a one-level processed data set.

[0030] With reference to the first aspect, in a possible implementation manner, the two-level DP-TBD processing on each frame data in the one-level processed data set comprises:

[0031] A set window length is obtained, and the one-level processed data set is grouped by using the set window length, and a second value function accumulation value in each group is calculated; A preset second threshold value is used to judge the second value function accumulation value in each group, if

[0032] the second value function accumulation value in each group is greater than the second threshold value, a measurement matrix corresponding to the last frame in each group is set to 0; if the second value function accumulation value in each group is less than the second threshold value, the measurement matrix corresponding to the last frame in each group is unchanged;

[0033] The measurement matrix that is not 0 is subjected to track backtracking to obtain a two-level processed data set.

[0034] With reference to the first aspect, in a possible implementation manner, the two-level data set is completed by comparing the data in the two-level processed data sets corresponding to adjacent windows to obtain a target track, comprising:

[0035] ​​​​​​​​​​​The first track and the second track corresponding to adjacent data in the secondary processing data set are compared, if the tracks are consistent, the linking is successful, the first track and the second track are stored in the first result set, if the tracks are inconsistent, an empty set is stored in the first result set, and the second track is stored in the second result set;

[0036] It is judged whether the second result set is empty, if yes, the first result set is output as a target track, if no, it is judged whether the track in the second result set is in a predicted gate, if yes, the second result set is filled in the first result set to obtain a target track, if no, a track is predicted according to the predicted gate and the first result set to obtain a target track.

[0037] In a second aspect, the present application provides a detection before tracking device for reducing target missing alarm probability of an airborne cognitive radar, which comprises:

[0038] A receiving processing unit is configured to perform data processing on echo data of a target received by an airborne radar, and perform space-time adaptive processing on the processed data to obtain a first data set; wherein the first data set comprises: frame data, each frame data comprising a plurality of PRF data, each PRF data comprising: a number of Doppler channels, a number of range gates and an amplitude value; and are greater than 0;

[0039] A constant false alarm detection unit is configured to perform constant false alarm detection on the first data set to obtain a target data set;

[0040] A data extension unit is configured to perform distance extension in a distance direction on each frame data in the target data set by using a maximum non-ambiguous distance of a radar to obtain an extended data set;

[0041] A first DP-TBD processing unit is configured to perform first DP-TBD processing between a plurality of PRF data in each frame data in the extended data set to obtain a first processing data set;

[0042] A second DP-TBD processing unit is configured to perform second DP-TBD processing between each frame data in the first processing data set by using a set window length to obtain a second processing data set;

[0043] An output unit is configured to complete the second data set by comparing data in the second processing data set corresponding to adjacent windows, and output a target track.

[0044] ​In a third aspect, the present application provides a pre-detection tracking server for reducing target false alarm probability of an airborne cognitive radar, comprising a memory and a processor;

[0045] The memory is used for storing computer executable instructions.

[0046] The processor is used for executing the computer executable instructions to realize the pre-detection tracking method for reducing target false alarm probability of the airborne cognitive radar.

[0047] In a fourth aspect, the present application provides a computer readable storage medium having executable instructions, and the computer executable instructions can realize the pre-detection tracking method for reducing target false alarm probability of the airborne cognitive radar when executed by a computer.

[0048] The one or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0049] (1) The present application improves the performance of target tracking and distance ambiguity resolution of the airborne cognitive radar for high-speed weak targets through two-stage TBD detection.

[0050] (2) The present application reduces the influence of CFAR false detection on target tracking and reduces the probability of target false alarm caused by missing some frame information through frame sliding window TBD method.

[0051] (3) The present application realizes the detection and tracking of multiple high-speed targets under the influence of clutter, and has very wide application range. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A pre-detection tracking method for reducing target false alarm probability of an airborne cognitive radar provided for an embodiment of the present application has a step flow chart;

[0053] Figure 2 A data diagram after two-stage DP-TBD processing provided for an embodiment of the present application;

[0054] Figure 3 A result diagram after clutter suppression and CFAR detection provided for an embodiment of the present application;

[0055] Figure 4 A detection and tracking result diagram of a traditional method provided for an embodiment of the present application;

[0056] Figure 5 A detection and tracking result diagram provided for an embodiment of the present application;

[0057] Figure 6 A detection probability comparison result diagram of 100 Monte Carlo experiments of two methods provided for an embodiment of the present application;

[0058] Figure 7 Figure 2 is a diagram of root mean square error comparison results of two methods provided by an embodiment of the present application in 100 Monte Carlo experiments. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0060] In a first aspect, the present application provides a detection-before-track method for reducing target false alarm probability of an airborne cognitive radar, as shown in Figure 1 The method comprises the following steps S101 to S106.

[0061] S101, performing data processing on echo data of a target received by an airborne radar, and performing space-time adaptive processing on the processed data to obtain a first data set; wherein the first data set comprises: frame data, each frame data comprising repetition frequency data, each repetition frequency data comprising: a number of Doppler channels, a number of range gates, and an amplitude value; and are all greater than 0.

[0062] Specifically, in step S101, the data processing is performed on the echo data of the target received by the airborne radar, and the space-time adaptive processing is performed on the processed data to obtain the first data set, comprising the following steps S1011 to S1013.

[0063] S1011, processing the echo data by using speed information of the target to obtain processed data.

[0064] S1012, performing space-time filtering, space-time estimation, and adaptive filtering on the processed data to obtain adaptive data.

[0065] S1013, performing clutter suppression on the adaptive data by using a cross-extended factorization method to obtain the first data set.

[0066] Exemplarily, in step S1013, the extended factorization method (EFA) is used for clutter suppression, and the optimal adaptive weight vector of the method is:

[0067] ;

[0068] wherein, is a covariance matrix after EFA processing, denotes a conjugate matrix. is a target-oriented vector after EFA processing, represents a reduced dimension time domain oriented vector, represents a spatial domain oriented vector, the output of the first Doppler channel is:

[0069] ;

[0070] Since the EFA is processed by using three adjacent Doppler channels, that is, the joint output data set of the adjacent three channels, that is, the first data set.

[0071] S102, constant false alarm detection is performed on the first data set to obtain a target data set.

[0072] Specifically, in step S102, constant false alarm detection is performed on the first data set to obtain a target data set, including the following steps S1021 to S1024.

[0073] S1021, converting the first data set to a plane to obtain a converted data set; wherein, represents a measurement matrix containing distance gate, Doppler channel number and amplitude information.

[0074] S1022, determining a to-be-detected data point in the converted data set and a reference data point corresponding to the to-be-detected data point, and calculating the clutter noise power around the target according to the average value of the to-be-detected data point and the reference data point; wherein, the reference data point is a plurality of data points adjacent to the to-be-detected data point, and the to-be-detected data point is any one data point in the converted data set. Here, the converted data set represents a data matrix, usually adjacent reference data points of the to-be-detected data point are obtained, if the adjacent reference data points cannot be obtained, the upper, lower, left and right row or column data are copied respectively.

[0075] S1023, determining a threshold factor corresponding to the to-be-detected data point, and combining the clutter noise power and the threshold factor to obtain a screening threshold.

[0076] S1024, screening the data in the first data set by using the screening threshold and the clutter noise power to obtain a target data set.

[0077] Illustratively, the average value of the reference data points around the to-be-detected data point is used to obtain the clutter noise power around the target in the radar beam :

[0078] ;

[0079] Multiply by the threshold factor :

[0080] ;

[0081] In the formula, This represents the false alarm rate. It is then compared with the detection unit, and the final output is:

[0082] ;

[0083] in, Indicates clutter noise power; Indicates the number of data frames; Indicates the first The power of each reference unit; Represents the threshold factor; express False alarm rate The number of reference units selected; Represents data points;

[0084] S103, using the radar's maximum unambiguous range, perform range extension in the range direction on each frame of data in the target dataset to obtain the extended dataset.

[0085] Specifically, in step S103, the range extension of each frame of data in the target dataset is performed in the range direction using the radar's maximum unambiguous range, resulting in an extended dataset, including:

[0086] The distance continuation formula is used to perform distance continuation on each frame of data in the target dataset. The distance continuation formula is expressed as:

[0087] ;

[0088] in, Indicates that the radar is in the Frame number Distance information derived from 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; Used to calculate the number of distance extensions; Represents the repetition frequency; indicates the frame number.

[0089] S104, performing first-level DP-TBD processing on the K-1 pulse repetition frequency data in each frame data in the extended data set to obtain a first-level processed data set.

[0090] Specifically, in step S104, the K-1 pulse repetition frequency data in each frame data in the extended data set is subjected to first-level DP-TBD processing to obtain a first-level processed data set, including the following steps S1041 to S1043.

[0091] S1041, converting the extended data set into a measurement matrix, and calculating the first value function accumulation value of each frame respectively. Here, the extended data set is revalued on the distance-Doppler K-1 pulse repetition frequency measurement matrix, and the value function accumulation of the first frame on the K-1 pulse repetition frequency is calculated as follows.

[0092] ;

[0093] ;

[0094] wherein, the measurement matrix includes distance gate, angle and amplitude information, indicates the state transition range from the state of the K-1 pulse repetition frequency to the state of the K pulse repetition frequency.

[0095] The X-axis of the measurement matrix is the number of Doppler channels, the Y-axis is the number of distance gates of each data point, and the size of the target corresponding to the coordinates of the data point is the amplitude value.

[0096] S1042, using a preset first threshold value to judge the first value function accumulation value of each frame data respectively, if , then the measurement matrix corresponding to the K-1 pulse repetition frequency data of the corresponding frame data is set to 0; if , then the measurement matrix corresponding to the K-1 pulse repetition frequency data of the corresponding frame data is not changed. Here, after the value function accumulation, the measurement matrix on the K-1 pulse repetition frequency is ​​​​​​​​​​​​​​​​​​​​​Threshold detection is performed:

[0097] ;

[0098] wherein, is a first threshold value, and the purpose is to screen out the final position where the value function accumulates CPI time.

[0099] S1043, track back for the measurement matrix which is not 0 to obtain a first processing data set. Here, track back is performed for all the measurement matrices which are not 0, and the TBD result of the first frame is obtained. Then, the distance and amplitude of the targets in the frame are averaged and re-assigned in the plane.

[0100] S105, second DP-TBD processing is performed between the frames in the first processing data set by using a set window length to obtain a second processing data set.

[0101] Specifically, second DP-TBD processing is performed between the frames in the first processing data set by using a set window length to obtain a second processing data set, including the following steps S1051 to S1053.

[0102] S1051, a set window length is obtained, and the first processing data set is grouped by using the set window length , and the second value function accumulation value in each group is calculated. Here, for the value function accumulation from the first frame to the last frame in the first processing data set:

[0103] ;

[0104] ;

[0105] wherein, represents the x-direction coordinate of the target (sin represents the y-direction); represents the first processing result of the first frame; represents the amplitude of the target in the first frame; represents the amplitude of the target in the last frame; represents the accumulated value function; represents the set window length; represents the number of data frames. S1052, a preset second threshold value is used to screen out the final position where the value function accumulates CPI time.

[0106] S1053, track back for the measurement matrix which is not 0 to obtain a second processing data set.​​​​​​​ accumulating the second value function values in each group respectively If , the measurement matrix corresponding to the last frame in each group is set to 0; if , the measurement matrix corresponding to the last frame in each group is unchanged. Here, after the value function accumulation, the last frame measurement matrix in each window is subjected to threshold detection:

[0107]

[0108] wherein is a second threshold value, and the purpose is to screen out the final position of the target in the frame time of the value function accumulation.

[0109] S1053, track back the measurement matrix which is not 0 to obtain a secondary processing data set. Here, track back all the measurement matrices which are not 0, that is, the TBD result with a length of can be obtained, wherein .

[0110] Exemplarily, the data obtained after setting the set window length for secondary DP-TBD processing is shown in Table 1. Figure 2

[0111] S106, by comparing the data in the secondary processing data sets corresponding to adjacent windows, the secondary data set is completed to obtain the target track.

[0112] Specifically, in step S106, by comparing the data in the secondary processing data sets corresponding to adjacent windows, the secondary data set is completed to obtain the target track, including the following steps S1061 to S1062.

[0113] S1061, compare the first track and the second track corresponding to the adjacent data in the secondary processing data set, if the tracks are consistent, the linking is successful, and the first track and the second track are stored in the first result set; if the tracks are inconsistent, an empty set is stored in the first result set, and the second track is stored in the second result set.

[0114] S1062, judge whether the second result set is an empty set, if yes, output the first result set as the target track; if no, judge whether the track in the second result set is in the predicted wave gate, if yes, fill the second result set into the first result set to obtain the target track; if no, perform track prediction according to the predicted wave gate and the first result set to obtain the target track.

[0115] Exemplarily, if the front of the first sliding window result is​​​​​​ The flight track information and the first The second to the third results of the sliding window The track information is consistent, that is:

[0116] ;

[0117] The link is considered successful, and the identical parts are stored in the final result. Inside.

[0118] If the link fails, store the empty set. The Within the current result, the linking of subsequent window results is further checked. If the linking is still successful, the portion with consistent track information is stored in the new result. Inside.

[0119] After all window results have been evaluated, if there are Then for Using data to predict flight paths, if Within the predicted gate, it is determined that... and For the same target, the flight path will Data completion Inside.

[0120] The effects of this invention are further illustrated by the following simulation experiments:

[0121] Simulation 1 demonstrates traditional detection and tracking processing for weak targets affected by ground clutter.

[0122] The simulation parameters of the radar system are shown in Table 1.

[0123] Table 1. Simulation parameters of the radar system

[0124]

[0125] Distance resolution can be calculated Maximum detection distance The maximum unambiguous distance gates for each repetition frequency are respectively Two moving targets are added to the scanned area, with initial distances of [distances to be filled in]. The speeds of motion are respectively A negative speed indicates that the aircraft is moving away from the carrier, while a positive speed indicates that the aircraft is moving closer to the carrier. Figure 3 The image shows the results after clutter suppression and CFAR (constant false alarm rate) detection. Figure 3 It can be observed that a target was missed when the radar scanned the 5th frame. Figure 4 The image shows the detection and tracking results of traditional methods. Figure 4It can be seen that the missed detection in the 5th frame resulted in a missed alarm for one target in the final estimation result.

[0126] Simulation 2: Detection results of the track after processing by this invention.

[0127] The parameter settings in this simulation are the same as those in Simulation 1. The simulation performs frame sliding window TBD processing, and the simulation results are shown below. Figure 5 .Depend on Figure 5 As can be seen, when processing the target on the right, this invention determines that the two track segments are the same target and merges them into a single target output. This invention not only avoids false alarms caused by missing detection of one target in the 5th frame, but also successfully achieves the detection and tracking of two high-speed, weak targets. Therefore, this simulation experiment proves that this invention can achieve the detection and tracking of multiple high-speed, weak targets even in the event of a missed detection.

[0128] Simulation 3 compares the method of increasing the state transition step size with the method of the present invention.

[0129] The parameter settings in this simulation are the same as those in Simulation 1. Since increasing the state transition step size during TBD processing can also reduce false alarms to some extent, this simulation conducts a comparative experiment between this method and the method of this invention: detection and tracking of high-speed, weak targets are performed respectively. To reduce random influences, 100 Monte Carlo experiments are conducted. The comparison results of detection probabilities are shown below. Figure 6 The results of the root mean square error comparison are shown in [link to relevant documentation]. Figure 7 .from Figure 6 and Figure 7 It can be seen that the method of increasing the state transition step size has a lower average detection probability and a higher average root mean square error than the present invention when dealing with weak targets under the influence of ground clutter. Therefore, the present invention is more effective in reducing the probability of missed detection of high-speed weak targets.

[0130] This invention provides a pre-detection tracking device for reducing the probability of missed detection by airborne cognitive radar. The device includes: a receiving and processing unit, a constant false alarm detection unit, a data extension unit, a first-level DP-TBD processing unit, a second-level DP-TBD processing unit, and an output unit.

[0131] The receiving and processing unit is used to process the echo data of the target received by the airborne radar, and to perform space-time adaptive processing on the processed data to obtain a first dataset; wherein, the first dataset includes: Frame data, each frame includes Each repetition frequency data point includes: the number of Doppler channels, the number of distance gates, and the amplitude value; and All are greater than 0.

[0132] A constant false alarm detection unit is configured to perform constant false alarm detection on the first data set to obtain a target data set.

[0133] A data extension unit is configured to perform distance extension on each frame of data in the target data set in the distance direction by using a maximum non-ambiguous distance of the radar to obtain an extended data set.

[0134] A first-level DP-TBD processing unit is configured to perform first-level DP-TBD processing between each pair of pulse repetition frequency data in each frame of data in the extended data set to obtain a first-level processed data set. A second-level DP-TBD processing unit is configured to perform second-level DP-TBD processing between each frame of data in the first-level processed data set by using a set window length to obtain a second-level processed data set.

[0135] An output unit is configured to complete the second-level processed data set by comparing data in the second-level processed data sets corresponding to adjacent windows and output a target track.

[0136] The apparatuses 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 apparatuses are described in the form of functions and various modules are described respectively. In the implementation of the present application, the functions of each module can be implemented in one or more software and / or hardware. Of course, the modules for implementing certain functions can also be implemented by a combination of multiple sub-modules or sub-units.

[0137]

[0138] ​The method, device or module 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 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, and 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 regarded as structures within the hardware component. Alternatively, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0139] Some modules in the device described in the present application can be described in the general context of computer-executable instructions, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform particular tasks or implement particular abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, including storage devices.

[0140] The present application provides a pre-detection tracking server for reducing target false alarm probability of an airborne cognitive radar, which comprises a memory and a processor; the memory is used for storing computer executable instructions; and the processor is used for executing the computer executable instructions to realize a pre-detection tracking method for reducing target false alarm probability of an airborne cognitive radar.

[0141] The present application provides a computer readable storage medium, which has executable instructions, and the computer executable instructions can realize a pre-detection tracking method for reducing target false alarm probability of an airborne cognitive radar.

[0142] The storage medium described above includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk drive (HDD), or a memory card. The storage medium can be used to store computer program instructions.

[0143] Although the present application provides the method operation steps as described in the embodiments or flowcharts, more or less operation steps can be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is only one of the many execution orders, and does not represent the only execution order. In actual device or client product execution, the method order 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).

[0144] 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 the necessary hardware. Based on such an understanding, the technical solutions of the present application, which are essential or contribute to the prior art, can be embodied in the form of a software product or through the implementation process of data migration. The computer software product can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for causing a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0145] The various embodiments in the specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment mainly explains the differences from other embodiments. The whole or part of the present application can be used in a plurality 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, small computers, large computers, distributed computing environments including any of the above systems or devices, etc.

[0146] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit 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 some or all of the technical features thereof can be replaced by equivalents; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar, characterized in that, include: Data processing is performed on the target echo data received by the airborne radar, and the processed data undergoes space-time adaptive processing to obtain a first dataset; wherein, the first dataset includes: Frame data, each frame includes Each repetition frequency data point includes: the number of Doppler channels, the number of distance gates, and the amplitude value; and All are greater than 0; Perform constant false alarm rate (CFAR) detection on the first dataset to obtain the target dataset; The range extension in the range direction of each frame of data in the target dataset is performed using the maximum unambiguous range of the radar to obtain the extended dataset; For each frame of data in the extended dataset Each repetition frequency (RF) data point undergoes first-level DP-TBD processing to obtain a first-level processed dataset; specifically, this includes: converting the extended dataset into a measurement matrix, calculating the first-value function accumulation value for each frame, and using a 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; track backtracking is performed on the non-zero measurement matrix to obtain the first-level processing dataset; Using a set window length, perform secondary DP-TBD processing on the data between each frame in the primary processing dataset to obtain the secondary processing dataset; By comparing the data in the secondary processing dataset corresponding to adjacent windows, the secondary processing dataset is completed to obtain the target trajectory. Specifically, this includes: comparing the first trajectory and the second trajectory corresponding to adjacent data in the secondary processing dataset; if the trajectories are consistent, the link is successful, and the first trajectory and the second trajectory are stored in the first result set; if the trajectories are inconsistent, an empty set is stored in the first result set, and the second trajectory is stored in the second result set; determining whether the second result set is empty; if so, the first result set is output as the target trajectory; if not, determining whether the trajectory in the second result set is within the prediction gate; if so, the second result set is filled into the first result set accordingly to obtain the target trajectory; if not, trajectory prediction is performed based on the prediction gate and the first result set to obtain the target trajectory.

2. The pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar according to claim 1, characterized in that, The process involves processing the target echo data received by the airborne radar and performing space-time adaptive processing on the processed data to obtain a first dataset, including: The echo data is processed using the velocity information of the target to obtain processed data; The processed data is subjected to spatiotemporal filtering, spatiotemporal estimation, and adaptive filtering to obtain adaptive data. The adaptive data is subjected to clutter suppression using a cross-extension factorization method to obtain the first dataset.

3. The pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar according to claim 1, characterized in that, The step of performing constant false alarm rate (CFAR) detection on the first dataset to obtain the target dataset includes: Convert the first dataset to Within the plane, the transformed dataset is obtained; where, This represents a measurement matrix containing distance gate, Doppler channel number, and amplitude information. The process involves determining the data point to be detected and the reference data point corresponding to the data point to be detected in the transformed dataset, and calculating the clutter noise power around the target based on the average value of the data point to be detected and the reference data point; wherein the reference data point is a plurality of data points adjacent to the data point to be detected, and the data point to be detected is any one data point in the transformed dataset. Determine the threshold factor corresponding to the data point to be detected, and combine the clutter noise power with the threshold factor to obtain the screening threshold; The data in the first dataset is filtered using the filtering threshold and the clutter noise power to obtain the target dataset.

4. The pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar according to claim 1, characterized in that, The method involves performing range extension in the range direction on each frame of data in the target dataset using the maximum unambiguous radar range to obtain the extended dataset, which includes: The distance extension formula is used to perform distance extension on each frame of data in the target dataset, wherein the distance extension formula is expressed as: ； in, Indicates the radar at 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; Used to calculate the number of distance extensions; Represents the repetition rate; Indicates the number of frames.

5. The pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar according to claim 1, characterized in that, The second-level DP-TBD processing is performed on the data between each frame in the first-level processing dataset using a set window length to obtain the second-level processing dataset, which includes: Get the set window length and using the set window length The primary processing dataset is grouped, and the cumulative value of the second-value function within each group is calculated; Using a preset second threshold value For each group, the cumulative value of the second-valued function Make a judgment, if Then the measurement matrix corresponding to the last frame in each group is set to 0; if Then the measurement matrix corresponding to the last frame in each group remains unchanged; Track backtracking is performed on the measurement matrix that is not zero to obtain the secondary processing dataset.

6. A pre-detection tracking device for airborne cognitive radar to reduce the probability of missed target detection, characterized in that, include: A receiving and processing unit is used to process the echo data of the target received by the airborne radar, and to perform space-time adaptive processing on the processed data to obtain a first dataset; wherein, the first dataset includes: Frame data, each frame includes Each repetition frequency data point includes: the number of Doppler channels, the number of distance gates, and the amplitude value; and All are greater than 0; The constant false alarm rate (CFAR) detection unit is used to perform CFAR detection on the first dataset to obtain the target dataset. The data extension unit is used to extend the range of each frame of data in the target dataset in the range direction using the maximum unambiguous range of the radar to obtain the extended dataset. The first-level DP-TBD processing unit is used to process the data in each frame of the extended dataset. Each repetition frequency (RF) data point undergoes first-level DP-TBD processing to obtain a first-level processed dataset; specifically, this includes: converting the extended dataset into a measurement matrix, calculating the first-value function accumulation value for each frame, and using a 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; track backtracking is performed on the non-zero measurement matrix to obtain the first-level processing dataset; The secondary DP-TBD processing unit is used to perform secondary DP-TBD processing on each frame of data in the primary processing dataset using a set window length to obtain the secondary processing dataset. The output unit is used to complete the secondary processing dataset by comparing the data in the secondary processing dataset corresponding to adjacent windows and output the target trajectory. Specifically, it includes: comparing the first trajectory and the second trajectory corresponding to adjacent data in the secondary processing dataset; if the trajectories are consistent, the link is successful, and the first trajectory and the second trajectory are stored in the first result set; if the trajectories are inconsistent, an empty set is stored in the first result set, and the second trajectory is stored in the second result set; determining whether the second result set is empty; if so, the first result set is output as the target trajectory; if not, determining whether the trajectory in the second result set is within the prediction gate; if so, the second result set is filled into the first result set to obtain the target trajectory; if not, trajectory prediction is performed based on the prediction gate and the first result set to obtain the target trajectory.

7. A pre-detection tracking server for reducing the probability of missed detection by airborne cognitive radar, 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 pre-detection tracking method for reducing the probability of missed detection by airborne cognitive radar as described in any one of claims 1-5.

8. 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 reducing the probability of missed detection by airborne cognitive radar as described in any one of claims 1-5.

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