CN116359906A - Automatic starting method for cross-period target morphology quality consistency inspection radar target - Google Patents
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Abstract
The invention discloses a method for automatically starting a cross-period target by checking the morphological quality consistency of radar targets. Sampling two points of different periods in a window point track pool according to a criterion of maximum period interval to initialize a target model, acquiring a predicted point position of the target model under each time stamp in a time window, establishing a corresponding measuring wave gate according to the predicted point position, searching other supporting points of the target model in the measuring wave gate of each time stamp point in the window point track pool, fitting all the target supporting points through a least square method, and determining whether the target model is a real moving target through a consistency check function. The above process of sampling and deciding is repeated until the trace of the maximum period interval is sampled. The method can minimize the detection root mean square error, and simultaneously calculates the proprietary wave gate of each different timestamp to reduce the calculation amount.
Description
Technical Field
The invention relates to a radar data processing technology in the technical field of radars, in particular to an automatic starting technology of a ship-borne radar target.
Background
The large number of random clutter points in a complex environment has a serious influence on target detection, so that the detection capability of modern radars faces a great challenge. The pre-detection Tracking (TBD) technology is an effective method for solving the problem of low signal-to-noise ratio motion weak point target detection, and essentially utilizes the continuity of the motion state of a target in space to perform non-coherent accumulation on multi-frame data by estimating a possible motion track of the target, so that the signal-to-noise ratio of detection is improved, and a real target is detected. TBD technology has been significantly developed and widely used in the field of small and weak target detection from the time of presentation. Researchers have proposed implementation methods of various TBD techniques, including mainly TBDs based on hough transform, multi-stage hypothesis testing, TBDs based on particle filtering, and TBDs based on dynamic programming. The original hough transformation technology is based on a Cartesian coordinate system, and when the method is applied to target detection of search radar, radar measurement coordinates are required to be converted into Cartesian coordinates for processing. Then, a polar coordinate Hough transformation method convenient for radar actual measurement data processing is provided, and the method can directly utilize the radial distance and azimuth angle of the radar actual measurement data to carry out Hough transformation without carrying out coordinate transformation on echo data into a Cartesian coordinate system, so that a variable speed moving target with a linear track in a radar detection area can be effectively detected and tracked, and the method has better robustness on the detection and tracking performance of the moving target with the linear track in a three-dimensional or high-dimensional space. The TBD algorithm based on multi-level hypothesis testing organizes all possible target trajectories in a tree form, updates, manages and tailors the tree by data per frame, and makes decisions using sequential probability ratio tests. The method has simple principle, is very convenient and is suitable for engineering application. However, under the condition of low signal-to-noise ratio, a large number of candidate track starting points are needed to reduce the probability of missed detection, so that the crotch at the back is rapidly increased, combined explosion occurs, the calculated amount of the algorithm is rapidly increased, the association is complex, and the performance of the algorithm is seriously affected. In the particle filter based pre-detection tracking (PF-TBD) algorithm, a discrete vector is used to describe the presence or absence of a target, and its state transition probability is calculated by a markov stationary random process. The composite state vector is then formed with it along with the hypothesized target state vector, and a hybrid estimation of them is achieved by the PF algorithm. And then researchers use different target models and noise models to carry out different improvements on the target models, in order to improve the target detection probability under the low signal-to-noise ratio, a dynamic programming-based TBD algorithm is used for realizing the track search of a weak target by multi-stage decision optimization, the selection of the value of a certain variable is required at each stage, the value is selected to reach the requirement that the whole process can reach the optimal according to a given criterion, and thus the global optimal can be approximated by single-step optimal. The TBD algorithm based on dynamic programming has the advantages of clear principle and easy realization of hardware, so that the TBD algorithm is widely applied to scenes with certain priori information, and a plurality of achievements are achieved in engineering. However, at a low signal-to-noise ratio, the target detection performance of the dynamic programming algorithm cannot be improved regardless of how the number of frames is increased.
Radar target initiation in complex environments faces mainly the following problems: 1) Because the excessive clutter point tracks affect the judgment of the normal target hypothesis test, the automatic initiation of false tracks is excessive; 2) Excessive clutter causes a rapid increase in computation, and the algorithm performance is severely degraded.
Disclosure of Invention
Aiming at the defects of the existing radar automatic starting technology, the invention provides the method for checking the radar target automatic starting through the cross-period target form quality consistency, solves the problems of excessive false track starting and excessive calculated amount, and meets the application requirements of actual engineering.
According to the method for automatically starting the cross-period target form quality consistency test radar target, provided by the invention, the target motion track is linear in a certain time and space range, the track in a time window is stored through a radar track sequence acquired in real time, the characteristic that parameters can be determined by utilizing the linear track only by two points of data is utilized, a two-point maximum time interval sampling consistency algorithm is designed under a hypothesis test framework, the two-point track is sampled in a hypothesis sampling stage to serve as point track pair data for hypothesis parameter estimation, a combined sampling mode is more advantageous than a random sampling mode under a heavy clutter environment, and the optimal estimation moment of the track sequence is obtained under the criterion that the minimum root mean square error is minimum based on consistency estimation. In the inspection stage in the sampling mode, according to different periods of data in a time sequence, due to residual distribution characteristics of two-point estimation, an optimal wave gate for inspection in different periods is adopted, so that the object can fall into the wave gate under the condition of eliminating clutter as much as possible, the detection probability of the object is ensured, and the point falling into the wave gate is taken as an object inner point. And finally, fitting all the target interior points by a least square method, and outputting the consistency check function which is larger than a threshold value as a real target. The specific technical scheme comprises the following steps:
acquiring radar trace data in real time, forming a trace data pool from the current period data and the radar trace data in the time window period, and storing the data in the trace data pool according to the period; modeling a target motion by utilizing a linear Gaussian process, initializing a target model by sampling two points of different periods in a window point track pool according to a criterion of a maximum period interval, acquiring a predicted point position of the target model under each time stamp in a time window, establishing a time-related measuring wave gate corresponding to the predicted point position by the predicted point position, searching other supporting points of the target model in the measuring wave gate of each time stamp point in the window point track pool, fitting all the target supporting points by a least square method, calculating the target form quality by utilizing a consistency check function, and determining whether the established target model is a real motion target or not by the form quality; the above process of sampling and deciding is repeated until the trace of the maximum period interval is sampled.
Further, the time-dependent measurement waveguide gate includes: assuming the variance sigma of the radar measurement error, k e { k per period k over a range of time window sizes w 1 ,k 2 ,k 3 ,...,k w The measurement wave gate of } is
Wherein the method comprises the steps of
G is the wave gate coefficient.
Further, the consistencyThe checking function includes: the point trace within the range of the time window with the size w is detected by a wave gate to obtain all point traces in the wave gate, and the time window k is assumed 1 To k w The sum of all trace points in the wave gate in the range is N, and the consistency check function of the corresponding target model is
Wherein:
wherein m is i (k) For the fitting position of the target model in the kth period, x i (k) For the trace point position closest to the fitting value in the kth periodic wave gate,
z i (k) The position of the trace point in the kth periodic wave gate; and taking the consistency check function of the target model as the morphological quality, and determining that the morphological quality is larger than a threshold value as a real target, otherwise, considering false.
The method can utilize situation characteristics of the targets and radar measurement error estimation to refine clutter points inside and outside the wave gate, and effectively inhibit false, thereby overcoming the influence of clutter on the detection of the moving targets, improving the detection performance of the moving targets in a heavy clutter region and greatly reducing the calculation cost.
Drawings
FIG. 1 is a preferred flow chart of the present invention.
FIG. 2 is a schematic illustration of the wave gate for different periods within a window.
Detailed Description
The invention is further explained below with reference to the drawings and the preferred embodiments.
The preferred implementation steps of the invention are shown in fig. 1, and are described as follows:
step 1: acquiring radar trace data in real time, forming a trace data pool from the current period data and the radar trace data in the time window period, and enabling the data in the trace data pool to be in accordance with period k 1 :k w Store, denoted as
o i Representing the set of all traces for the corresponding period.
Step 2: sampling the kth 1 One point of the period is p (k) 1 ) Sample the kth w One point of the period is p (k) w ) Initializing a linear motion model by using the two points as a motion target hypothesis, namely, the position of the motion target in the kth period in the time window is as follows:
wherein the method comprises the steps of
Step 3: calculating a measurement detection wave gate of the moving object corresponding to each period, assuming a variance sigma of a measurement error of the radar, and performing a time window k 1 :k w Within the range of (1), the measurement wave gate of each period k is
Where G is the wave gate coefficient, as shown in fig. 2.
Step 4: within the trace-spot pool, time window k 1 :k w Point P in w The point trace in the wave gate is obtained through the wave gate screening in the last step, namely the point trace z (k) in the wave gate with the kth period and the time window k 1 :k w The total number of the inner traces of all the wave gates is N.
Step 5: searching other support points of the target model in the measuring wave gate of each period in the window point trace pool, namely selecting target positions of Euclidean distance and kth period in the points o (k) corresponding to each period kDevice for placing articles
The nearest point, the trace of the support point for the target, is denoted as x (k), time window k 1 :k w The total number of all support traces in the network is n.
Step 6: window time k 1 :k w All support traces x (k) 1 :k w ) And obtaining a fitting linear track through least square linear fitting, and obtaining a fitting position m (k) of the corresponding track corresponding to the kth period.
Step 7: consistency judgment is carried out on the target model, and the consistency check function of the target model is that
Wherein,,
step 8: and taking the consistency check function of the corresponding target model as the morphological quality, and determining that the morphological quality is larger than a threshold value as a real target, otherwise, considering false. Wherein the threshold is set to be Q/w, Q is an evaluation coefficient, and the value is generally greater than 50, and w is the time window size.
Step 9: repeating the sampling and deciding processes of the steps 2 to 8 until the kth 1 Periodic trace and kth w The trace points of the period are completely sampled, and the operation of the current period is ended.
Claims (3)
1. The automatic starting method for cross-period target morphology quality consistency inspection radar targets is characterized by comprising the following steps of: acquiring radar trace data in real time, forming a trace data pool from the current period data and the radar trace data in the time window period, and storing the data in the trace data pool according to the period; modeling a target motion by utilizing a linear Gaussian process, initializing a target model by sampling two points of different periods in a window point track pool according to a criterion of a maximum period interval, acquiring a predicted point position of the target model under each time stamp in a time window, establishing a time-related measuring wave gate corresponding to the predicted point position by the predicted point position, searching other supporting points of the target model in the measuring wave gate of each time stamp point in the window point track pool, fitting all the target supporting points by a least square method, calculating the target form quality by utilizing a consistency check function, and determining whether the established target model is a real motion target or not by the form quality; the above process of sampling and deciding is repeated until the trace of the maximum period interval is sampled.
2. The method for automatically starting cross-cycle target morphology quality consistency check radar targets of claim 1, wherein: the time-dependent measurement waveguide gate includes: assuming the variance sigma of the radar measurement error, k e { k per period k over a range of time window sizes w 1 ,k 2 ,k 3 ,...,k w The measurement wave gate of } is
Wherein the method comprises the steps of
G is the wave gate coefficient.
3. The method for automatically starting cross-cycle target morphology quality consistency check radar targets of claim 1, wherein: the consistency check function includes: the point trace within the range of the time window with the size w is detected by a wave gate to obtain all point traces in the wave gate, and the time window k is assumed 1 To k w The sum of all trace points in the wave gate in the range is N, corresponding to the target modelThe consistency check function is
Wherein:
wherein m is i (k) For the fitting position of the target model in the kth period, x i (k) For the trace point position closest to the fitting value in the kth periodic wave gate,
z i (k) The position of the trace point in the kth periodic wave gate; and taking the consistency check function of the target model as the morphological quality, and determining that the morphological quality is larger than a threshold value as a real target, otherwise, considering false.
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| CN117784028A (en) * | 2024-02-27 | 2024-03-29 | 南京天朗防务科技有限公司 | Random clutter recognition method, system, computer device and storage medium |
| CN117784028B (en) * | 2024-02-27 | 2024-05-28 | 南京天朗防务科技有限公司 | Random clutter recognition method, system, computer device and storage medium |
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