CN111413693A - TBD (tunnel boring device) and conventional tracking combination method based on double-threshold shunt processing in MIMO (multiple input multiple output) radar - Google Patents

TBD (tunnel boring device) and conventional tracking combination method based on double-threshold shunt processing in MIMO (multiple input multiple output) radar Download PDF

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CN111413693A
CN111413693A CN202010281675.3A CN202010281675A CN111413693A CN 111413693 A CN111413693 A CN 111413693A CN 202010281675 A CN202010281675 A CN 202010281675A CN 111413693 A CN111413693 A CN 111413693A
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CN111413693B (en
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黄勇
丁昊
刘宁波
陈小龙
薛永华
张海
张�林
董云龙
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Naval Aeronautical University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract

The invention discloses a TBD and conventional tracking combination method based on double-threshold shunt processing in an MIMO radar, which belongs to the technical field of crossing radar signal processing and data processing and comprises the following steps: double-threshold detection is carried out, shunting parallel processing of a conventional tracking channel and a TBD channel is realized, then characteristic information of the point track is utilized to assist data association, the point track which is already associated is eliminated by utilizing a processing result of the conventional tracking channel, the conventional tracking track and the TBD track are output, and track fusion is carried out; the method combines TBD processing and conventional tracking processing, so that the radar target tracking process can simultaneously consider strong and weak targets; and the problem of difficult data association caused by intensive threshold points is solved in the track searching process of TBD, so that the practicability of the method is ensured, and the method has popularization and application values.

Description

TBD (tunnel boring device) and conventional tracking combination method based on double-threshold shunt processing in MIMO (multiple input multiple output) radar
Technical Field
The invention relates to the technical field of radar signal processing and data processing intersection, in particular to a method for combining a TBD process and a conventional tracking process based on double-threshold shunt processing in an MIMO radar.
Background
The radar is used as a main means of target detection and is widely applied to the fields of public safety and national defense safety. However, under the influence of factors such as observation conditions, radar parameters, interference environments, target characteristics and the like, the target echo amplitudes often show great differences in the observation scene. Some target echoes are strong, and a detection result can be obtained through a normal detection threshold; and some target echoes are very weak, and detection results can be obtained only by improving the false alarm rate and reducing the threshold, so that the number of threshold-passing points is greatly increased, the reliability of data association in the subsequent tracking process is seriously influenced, and meanwhile, the target tracking system is very easy to overload. Therefore, the conventional target tracking process cannot be compatible with the detection of weak targets.
The MIMO radar is a novel radar system, and realizes a wide-transmitting and narrow-receiving working mode by transmitting orthogonal waveforms or approximate orthogonal waveforms, thereby providing a foundation for accumulating target energy for a long time and improving the detection capability of the radar to small targets. In the long-time accumulation processing of the MIMO radar, a practical accumulation mode is a combined accumulation mode combining short-time coherent accumulation and long-time non-coherent accumulation, wherein Track-Before-detection-Detect (TBD) processing is long-time non-coherent accumulation processing, and the detection performance of the MIMO radar on a weak target is further improved through the long-time non-coherent accumulation on the basis of improving the signal-to-noise-ratio through the short-time coherent accumulation.
The TBD processing is a common technology for solving the problem of weak target detection, and the basic idea is to judge whether a target exists or not by searching a moving track of the target within a time window. The basic reason that the TBD processing can improve the target detection performance is that in the process of multiple scans, the target threshold point has strong correlation in position, and thus can form a track, while the false alarm point has low correlation in position, and thus is difficult to form a track.
The TBD processing requires two levels of detection thresholds in the implementation. The first-stage detection threshold is called the first threshold for short, and is used for threshold judgment of detection statistics formed after processing of matched filtering, anti-interference, clutter suppression and the like is carried out on echo signals acquired by single scanning. The so-called CFAR detection threshold refers to this level of detection threshold. Under the condition that elements such as a radar system, an observation scene, a CFAR detection method and the like are determined, the detection threshold value can be obtained through the false alarm probability given by the system, for example, under the background of Rayleigh clutter, the false alarm probability is 10-6And when the detection probability is 0.5, the detection threshold corresponding to the CA-CFAR is 12.5 dB. The term "normal detection threshold" as used herein refers to a detection threshold corresponding to a false alarm probability required by a radar system. However, for TBD processing, the detection threshold value (i.e. the "lower detection threshold" in the present invention, i.e. the "first threshold" in TBD terminology) must be lowered to solve the weak target detection problem, otherwise the weak target is difficult to pass the threshold, and thus the track cannot be formed. The second-level detection threshold of the TBD processing is referred to as a second threshold, and is a detection threshold used when threshold decision is performed after statistical quantity (for example, an amplitude accumulated value of all points on a flight path) is formed from a flight path obtained by search. In the TBD processing, the target can be judged to exist only when the track statistic passes a second threshold, and the target track is given.
The TBD processing can solve the detection problem of the weak target to a certain extent, and simultaneously provides a target track, and the target track searching process in the TBD processing is very similar to a radar target tracking process. Then, a natural idea is to replace the conventional tracking procedure with TBD processing. This concept seems to be feasible if the computational burden and the memory burden are not considered and the problem that the difficulty of correctly correlating the data is increased due to the reduction of the first threshold is ignored, because the TBD process can simultaneously obtain the tracks of the strong target and the weak target. However, the computational resources and memory resources that can be provided by a practical radar system are very limited, and thus this concept is basically not feasible. The TBD process is able to detect weak targets and their motion trajectories, largely because the time window limits the computational and memory burdens it requires to an affordable range; and the track processing in the conventional tracking process is carried out along with the running of the radar without the limitation of a time window, so that the calculation load and the storage load are inevitably beyond the bearable range of the radar system. In addition, when the radar system does not need to be concerned about weak target detection, it is not necessary to employ TBD processing. Therefore, in an actual radar system, the TBD processing should be designed as an optional item, and when a weak target needs to be detected, the TBD processing is integrated in a conventional tracking flow; and when the weak target does not need to be detected, the TBD can be effectively stripped from the conventional tracking flow conveniently.
Disclosure of Invention
The invention aims to provide a TBD (tunnel boring detector) and conventional tracking combination method based on double-threshold shunt processing in an MIMO (multiple input multiple output) radar, which is a method for fusing TBD processing in the conventional tracking process, so that the TBD processing can be conveniently incorporated into the whole target tracking process when a weak target needs to be detected by an MIMO radar system, and the TBD processing can be effectively stripped out of a target tracking system when the weak target does not need to be detected, and the normal work of the tracking system is not influenced. The technical problems to be solved include:
(1) the separation and fusion mechanism problem of TBD processing process and conventional tracking process;
(2) when a target track is searched in TBD processing, the data association problem is solved under the condition that threshold points are dense;
(3) the fusion problem of TBD track and conventional tracking track;
in order to solve the above problems, the present invention provides the following solutions: the invention provides a method for combining TBD (tunnel boring device) based on double-threshold shunt processing and conventional tracking in an MIMO (multiple input multiple output) radar, which comprises the following steps of:
step 1, double-threshold detection: separating and parallel processing of conventional tracking and TBD processing channels are realized by using detection statistic data before the k-th frame threshold detection and setting a normal detection threshold and a lower detection threshold, wherein the conventional tracking channel corresponds to the normal detection threshold, and the TBD processing channel corresponds to the lower detection threshold;
step 2, extracting trace point agglomeration and trace point information: respectively carrying out trace point condensation and trace point information extraction in a TBD processing channel and a conventional tracking channel;
step 3, outputting a conventional tracking track: in a conventional tracking channel, target tracking processing and first-class online track quality evaluation of a k-th frame are completed by utilizing a target comprehensive track formed by cutting to the k-1 th frame and trace point data of the k-th frame, an output track with quality passing through is called a conventional tracking track, meanwhile, trace point information which is associated with the target track in the k-th frame is transmitted to a TBD processing channel, and the trace point information is deleted from the TBD processing channel;
and 4, outputting a TBD track: in a TBD processing channel, performing sliding window type TBD processing by using point track data obtained by scanning from the kth-N +1 to the kth frame for N times to form a track segment with the length not greater than N; then, carrying out second type online track quality evaluation on the track segments, wherein the output track with over-quality is called TBD track;
step 5, track fusion: performing track fusion on the TBD track and the conventional tracking track to form a target comprehensive track cut to the kth frame;
and 6, circulating: and entering a (k + 1) th frame, and repeating the steps from 1 to 5.
Preferably, CFAR detection is performed by using CFAR detection statistics respectively through double thresholds, and each CFAR detection statistic T(r,θ)The formula for performing threshold detection is as follows:
Figure BDA0002446803270000041
wherein (r, θ) represents a distance-azimuth resolving unit,
Figure BDA0002446803270000042
indicating that there is a target hypothesis in the (r, theta) cell to be detected,
Figure BDA0002446803270000043
indicating no target hypothesis in the (r, theta) cell to be detected, and η indicating a detection threshold.
Preferably, in the conventional tracking path, η is determined by the required false alarm probability of the radar system, referred to as the "normal detection threshold", and the corresponding false alarm probability is set to 10-4Or 10-6；
In the TBD processing path, η refers to the first threshold, which is usually lower than the "normal detection threshold" according to the TBD algorithm requirement, and is called the "lower detection threshold", and the corresponding false alarm probability is usually 10-1Or 10-2Magnitude.
Preferably, the trace point agglomeration process is: for the trace point data passing through the threshold, smoothing is respectively carried out by adopting a smoothing window in two dimensions of distance and direction to form new trace point data, and then trace point agglomeration is carried out by utilizing an 8-connection rule, namely, the trace point data is regarded as the trace point from the same target only when the trace point meets the 8-connection rule;
the trace point information extraction process comprises the following steps: and measuring the trace points from the same target, wherein the measurement information comprises the distance-azimuth position of the trace point centroid, the distance-azimuth span ratio of the trace points, the number of points in the trace points, the compactness of the trace points and the coincidence degree of the trace point centroid and the geometric center.
Preferably, the distance-azimuth position (r) of the centroid of said traceCenter of mass,θCenter of mass)：
Figure BDA0002446803270000044
The distance-azimuth span ratio of the point trace, namely the ratio l of the distance span to the azimuth span of the point trace:
Figure BDA0002446803270000045
the number of points in the trace, i.e. the number of threshold points belonging to the same target, is recorded as: s;
the compactness of the trace points reflects the position distribution condition of the trace points in the distance-azimuth span, and is defined as the product f of the number of the trace points and the distance-azimuth span:
Figure BDA0002446803270000051
the coincidence degree of the trace point centroid and the geometric center reflects the distribution condition of the trace point amplitude along with the position in the distance-azimuth span, and is defined as the Euclidean distance c between the trace point centroid and the geometric center:
Figure BDA0002446803270000052
wherein d isrRepresents the distance sampling interval, dθIndicating the angular separation between adjacent azimuth lines,
Figure BDA0002446803270000053
Figure BDA0002446803270000054
preferably, on the basis of extracting trace point information and forming a measurement, calculating a correlation degree of the measurement:
① centroid position (r) as measured by time kCenter of mass,θCenter of mass) Judging whether the measurement falls into a candidate wave gate of the flight path, and if the measurement falls into the candidate wave gate, calling the measurement as candidate measurement;
②, calculating the similarity of the target measurement at the time k-1 and the candidate measurement at the time k on four characteristic information, namely a distance-azimuth span ratio l, the number of points in the trace s, the trace compactness degree f and the coincidence degree c of the trace centroid and the geometric center.
Preferably, the calculating process of the similarity in the step (2) is as follows:
① the distance-azimuth span ratio of the m-th candidate measurement at the time k and the target measurement at the time k-1 is set as lm(k) And l (k-1), defining the similarity of the distance-azimuth span ratio of the two measurements
Figure BDA0002446803270000055
Comprises the following steps:
Figure BDA0002446803270000056
② points of the m-th candidate measurement at the time k and the target measurement at the time k-1 are respectively set as sm(k) And s (k-1), defining the similarity of the two measured points
Figure BDA0002446803270000057
Is composed of
Figure BDA0002446803270000058
③ points of the m-th candidate measurement at the k moment and the target measurement at the k-1 moment are respectively set to be compact degreesm(k) And f (k-1), defining the similarity of the compactness of the two measured traces
Figure BDA0002446803270000061
Comprises the following steps:
Figure BDA0002446803270000062
④, c is the coincidence degree of the centroid and the geometric center of the m-th candidate measurement at the moment k and the target measurement at the moment k-1m(k) And c (k-1), defining the similarity of the coincidence degree of the two measured mass centers and the geometric center
Figure BDA0002446803270000063
Comprises the following steps:
Figure BDA0002446803270000064
finally, calculating the comprehensive association degree of the mth candidate measurement at the moment k and the target measurement at the moment k-1, and associating the candidate measurement with the maximum comprehensive association degree with the target measurement at the moment k-1;
similarity degree assigned to four characteristic information
Figure BDA0002446803270000065
The weights of (A) are respectively recorded as α1、α2、α3、α4And α1+α2+α3+α4Defining the comprehensive association degree of the m-th candidate measurement at the moment k and the target measurement at the moment k-1 as ξm(k)：
Figure BDA0002446803270000066
Preferably, the on-line track quality assessment comprises second threshold detection in TBD processing, and the comprehensive quality of the track is assessed from track continuity, field value number and maximum continuous interruption time length.
Preferably, in the track fusion: and aiming at the moment k, fusing all conventional tracking tracks output by a conventional tracking channel and all TBD tracks output by a TBD processing channel:
①, taking out any conventional tracking Track, wherein the measurement number of the Track in the interval [ k-N +1, k ] is P, searching tracks with the measurement coincidence ratio of P/2 to the Track in all TBD tracks, and recording the number as N, wherein N is more than or equal to 0;
if n is 0, outputting the Track as a target integrated Track cut to the k-th frame;
if N is greater than 0, aiming at the time t, t ∈ [ k-N +1, k ], if the measurement of the time t on the Track is empty, the measurement points at the corresponding time on the N TBD tracks are searched for supplementation, if the number of the measurement points is more than 1, the point closest to the Track is selected for supplementation, if the number of the measurement points is 0, the supplementation is not carried out, and the N TBD tracks are deleted after the time t is traversed;
② following the steps in ①, traversing all conventional tracking tracks in turn;
③ all the conventional tracking tracks and the residual TBD tracks obtained after ① and ② processing are taken as target comprehensive tracks of the kth frame, sent to a conventional tracking processing module and enter the conventional tracking processing link of the (k + 1) th frame.
The invention discloses the following technical effects:
(1) the invention provides a method for combining TBD processing and conventional tracking processing, so that a strong target and a weak target can be considered simultaneously in the radar target tracking process;
(2) the method adopts sliding window type TBD processing, and two measures are adopted in the track searching process of the TBD to relieve the problem of difficult data association caused by intensive threshold points, so that the practicability of the method is ensured, wherein the two measures are that firstly, the characteristic information of the track points is extracted and utilized to assist data association, and secondly, the processing result of a conventional tracking channel is utilized to eliminate the associated track points;
(3) the invention adopts on-line track quality evaluation, and feeds back the evaluation result to the target tracking system for guiding the strategy selection in the steps of track extrapolation, wave gate design and the like and the strategy selection in the fusion process of TBD track and conventional tracking track, thereby improving the quality of the output track.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a schematic diagram of the distribution of target traces after distance-azimuth domain smoothing in the present invention,
in FIG. 2, "cross" indicates the point after the threshold is crossed, and "circle" indicates the diffusion point after smoothing; Δ r is the distance span of the trace points and is expressed by the number of points extending on the distance; delta theta is the azimuth span of the point trace and is expressed by the number of points extending in the azimuth; the magnitude of the passing threshold point in the jth azimuth cell of the ith distance cell is recorded as
Figure BDA0002446803270000071
riAnd thetajRespectively representing the distance value and the azimuth value corresponding to the ith distance unit and the jth azimuth unit.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
In order to explain the processes involved in the present invention and simplify the description of the processes, the following terms are first agreed.
① "Normal detection threshold", wherein the false alarm probability in the field of CFAR detection of radar target determines the detection threshold, the detection threshold obtained from the false alarm probability required by radar system is called "Normal detection threshold", and in most radar factory specifications, the false alarm probability required by radar system is usually set to 10-4Or 10-6。
② "first threshold" and "second threshold". TBD processing usually includes two-level threshold detection, the "first threshold" corresponds to the first level threshold detection, in order to improve the detection probability of weak target, the "first threshold" is usually lower than the "normal detection threshold", which is called as "lower detection threshold" in the present specification, and the corresponding false alarm probability is usually 10-1Or 10-2Magnitude; the "second threshold" corresponds to the second-level threshold detection, which is a detection threshold used when the threshold decision is made for the track statistic (for example, the amplitude accumulation value of all points on the track); in the TBD processing, the target can be judged to exist only when the track statistic passes a second threshold, and the target navigation is givenTracing; and the total false alarm probability corresponding to the TBD processing is jointly determined by the first threshold and the second threshold.
③ frame, wherein a two-coordinate (e.g., range-azimuth two-coordinate) radar scan around the surveillance area once is referred to as a "scan", all range-azimuth cell data obtained in the observation area is referred to as a "frame" of data, and frame k represents all range-azimuth cell data obtained in the observation area from the kth scan of the radar.
④ "trace of points" after threshold detection, a blob of points consisting of a plurality of distance-orientation passing threshold points that satisfy the 8-connectivity rule is called a "trace of points".
⑤ "metrology" is a measurement point formed after trace condensation, including five characteristic information of distance-azimuth position of trace centroid, distance-azimuth span ratio of trace, number of points in trace, trace compactness, and coincidence of trace centroid and geometric center.
Referring to fig. 1, the invention provides a method for combining TBD based on dual-threshold shunt processing and conventional tracking in MIMO radar, comprising the following steps:
step 1, threshold detection comprises two parallel links of ' passing normal detection threshold ' and ' passing lower detection threshold
At a radar receiving end, after received echo signals are usually subjected to amplification mixing, sampling and digitization, matched filtering, anti-interference, clutter suppression and other processing links, a detection statistic with a CFAR characteristic is formed in each distance-direction resolution unit and is recorded as T(r,θ)Where the subscript (r, θ) denotes the distance-azimuth resolving unit. For each CFAR detection statistic, the following threshold detection is performed:
Figure BDA0002446803270000091
wherein,
Figure BDA0002446803270000092
indicating that there is a target hypothesis in the (r, theta) cell to be detected,
Figure BDA0002446803270000093
indicating no target hypothesis in the (r, theta) cell to be detected, and η indicating a detection threshold.
In a conventional tracking channel, η is determined by the false alarm probability required by a radar system and corresponds to a link of 'passing a normal detection threshold';
in the TBD processing path, η is the first threshold, which is usually lower than the normal detection threshold according to the requirement of the TBD algorithm, and thus corresponds to the "passing of the lower detection threshold" step.
After the detection statistics in each distance-direction distinguishing unit respectively passes through a normal detection threshold and a lower detection threshold, the detection statistics respectively enter a conventional tracking channel and a TBD processing channel, and the two paths of detection statistics are processed in parallel.
Step 2, point trace condensation and point trace information extraction
And for the trace point data after the threshold is crossed, smoothing the trace point data by adopting smoothing windows of [0.3,0.4 and 0.3] in two dimensions of distance and direction respectively to form new trace point data, and then performing trace point agglomeration by utilizing an 8-connection rule, namely, only when the trace point meets the 8-connection rule, the trace point data is regarded as the trace point from the same target.
Aiming at the point traces from the same target, extracting five characteristic information of the distance-azimuth position of the centroid of the point traces, the distance-azimuth span ratio of the point traces, the number of points in the point traces, the compactness of the point traces and the coincidence degree of the centroid of the point traces and the geometric center to form the measurement of the target. The five pieces of feature information are defined below with reference to the schematic diagram of the distribution of the target point traces after the distance-azimuth domain smoothing as shown in fig. 2. In the figure, "cross mark" represents the point after threshold crossing, and "circle" represents the diffusion point after smoothing; Δ r is the distance span of the trace points and is expressed by the number of points extending on the distance; delta theta is the azimuth span of the point trace and is expressed by the number of points extending in the azimuth; the magnitude of the passing threshold point in the jth azimuth cell of the ith distance cell is recorded as
Figure BDA0002446803270000101
riAnd thetajRespectively representing the distance value and the azimuth value corresponding to the ith distance unit and the jth azimuth unit.
① distance-azimuth position of trace centroid (r)Center of mass,θCenter of mass)：
Figure BDA0002446803270000102
② distance-azimuth span ratio of the trace points, i.e., the ratio of the trace point distance span to the azimuth span:
Figure BDA0002446803270000103
③ points in the trace, i.e., the number of threshold points that belong to the same target, is denoted as s.
④ compactness degree, which reflects the position distribution of the trace points in the distance-azimuth span, is defined as the product of the number of points in the trace points and the distance-azimuth span:
Figure BDA0002446803270000104
⑤ coincidence degree of centroid and geometric center of point trace, which reflects distribution of amplitude of point trace along with position in distance-azimuth span, is defined as Euclidean distance between centroid and geometric center
Figure BDA0002446803270000105
Wherein d isrRepresents the distance sampling interval, dθIndicating the angular separation between adjacent azimuth lines,
Figure BDA0002446803270000106
step 3, forming a track, including two links of 'conventional tracking processing' in a conventional tracking channel and 'searching a target track by using the latest N frames of measured data' in a TBD processing channel
Both links are used to form the target track. Any target tracking algorithm commonly used in the existing radar system can be adopted in the routine tracking processing link, and the difference is that the data association part utilizes other four measured characteristic information extracted in the step 2 besides the measured position information. In the track searching link in the TBD processing channel, a sliding window type TBD processing algorithm based on dynamic programming is adopted, the algorithm is sequential, the length of a sliding window is N, namely the target track is searched by utilizing the measured data of the k-N +1 th frame to the k-th frame, and the track searching process is similar to the conventional tracking processing, wherein k is the current frame number. Compared with the conventional tracking processing, the data association difficulty in the track searching link in the TBD processing is higher, and the threshold passing measurement is more because the channel adopts a lower detection threshold.
The invention adopts two measures to solve the data association problem in the track searching link of the TBD processing channel. One is to delete the associated target measurements in the TBD processing channel using the track processing result of the k-th frame in the conventional tracking channel. Secondly, the measured characteristic information is used for association, firstly, the characteristic similarity between the candidate measurement falling into the correlation wave gate at the kth moment and the measurement at the kth-1 moment is calculated, then, the comprehensive association degree is calculated according to the characteristic similarity and the characteristic weight, and the candidate measurement with the maximum comprehensive association degree is selected for association, wherein the specific steps are as follows:
first, according to the centroid position (r) measured at the time kCenter of mass,θCenter of mass) Judging whether the measurement falls into a candidate wave gate of the flight path, and if the measurement falls into the candidate wave gate, calling the measurement as candidate measurement;
then calculating the similarity of the target measurement at the k-1 moment and the candidate measurement at the k moment on four characteristic information including a distance-azimuth span ratio l, the number of points in the point trace s, the compact degree f of the point trace and the coincidence degree c of the centroid of the point trace and the geometric center, and specifically comprising the following steps of:
① the distance-azimuth span ratio of the m-th candidate measurement at the time k and the target measurement at the time k-1 is set as lm(k) And l (k-1), defining the similarity of the distance-azimuth span ratio of the two measurements
Figure BDA0002446803270000111
Comprises the following steps:
Figure BDA0002446803270000112
② points of the m-th candidate measurement at the time k and the target measurement at the time k-1 are respectively set as sm(k) And s (k-1), defining the similarity of the two measured points
Figure BDA0002446803270000113
Comprises the following steps:
Figure BDA0002446803270000114
③ points of the m-th candidate measurement at the k moment and the target measurement at the k-1 moment are respectively set to be compact degreesm(k) And f (k-1), defining the similarity of the compactness of the two measured traces
Figure BDA0002446803270000115
Comprises the following steps:
Figure BDA0002446803270000121
④, c is the coincidence degree of the centroid and the geometric center of the m-th candidate measurement at the moment k and the target measurement at the moment k-1m(k) And c (k-1), defining the similarity of the coincidence degree of the two measured mass centers and the geometric center
Figure BDA0002446803270000122
Comprises the following steps:
Figure BDA0002446803270000123
and finally, calculating the comprehensive association degree of the mth candidate measurement at the moment k and the target measurement at the moment k-1, and associating the candidate measurement with the maximum comprehensive association degree with the target measurement at the moment k-1. Different feature information similarity degrees are required to be assigned with different weights due to different importance degrees of different feature information, and then, the similarity degrees are assigned to the four feature information
Figure BDA0002446803270000124
Figure BDA0002446803270000125
The weights of (A) are respectively recorded as α1、α2、α3、α4And α1+α2+α3+α4Defining the comprehensive association degree of the mth candidate measurement at the moment k and the target measurement at the moment k-1 as ξm(k)：
Figure BDA0002446803270000126
And 4, evaluating the track quality, namely evaluating the first type of online track quality in a conventional tracking channel and evaluating the second type of online track quality in a TBD processing channel
The first type of online track quality evaluation in the conventional tracking channel refers to that aiming at a target track obtained in the previous link, the comprehensive quality of the track is evaluated from three aspects of track continuity, wild value quantity and maximum continuous interruption time, and the track with higher comprehensive quality is selected to enter the next link. It should be noted that the track at the track start stage does not perform the "first-type online track quality evaluation" and directly enters the next link.
The second type of on-line track quality evaluation in the TBD processing channel comprises second threshold detection specific to TBD processing, and the comprehensive quality of the track is evaluated from three aspects of track continuity, wild value quantity and maximum continuous interruption time for the track passing through the second threshold, and the track with higher comprehensive quality is selected to enter the next link.
① second threshold detection in TBD processing
And setting TBD (tunnel boring device) processing to search a certain track, wherein the track comprises P (P is less than or equal to N) measurements, and the amplitude (the value is the maximum value of the amplitude of the point track corresponding to the measurement) of each measurement is recorded as xpP is 1, …, P. The second threshold detection is defined as:
Figure BDA0002446803270000131
wherein,
Figure BDA0002446803270000132
indicating that the track is a target track,
Figure BDA0002446803270000133
indicating that the track is not a target track; gamma denotes a second threshold. When in use
Figure BDA0002446803270000134
When the track is established, the track enters the subsequent track quality evaluation.
② track continuity
Setting time k to obtain a flight path containing P measurements, the 1 st measurement corresponding to the T1Sub-scan, time k corresponding to the T-th timekFrom scan to scan, then track continuity is recorded as L, defined as:
Figure BDA0002446803270000135
③ field rate
Let k time point obtain a certain track, the track contains P measurement, the coordinate of the P (P is 1, …, P) measurement is (r)xp,θxp) (ii) a Performing least square fitting on the flight path by using a quadratic curve to obtain P fitting points at corresponding moments after fitting, wherein the coordinate of the P-th fitting point is (r)yp,θyp) (ii) a Calculating Euclidean distance R point by pointp：
Figure BDA0002446803270000136
If R isp>, then the p-th point is considered as a outlier point, wherein the outlier point represents the Euclidean distance threshold; and traversing P measurements on the flight path to obtain the outlier quantity Q. Then, outlier rate is defined as:
Figure BDA0002446803270000137
④ maximum continuous discontinuity ratio
And setting the time k to obtain a certain track, wherein the track comprises P measurement, and recording the maximum continuous lost point on the track as G in the period from the time corresponding to the 1 st measurement of the track to the time k. Then, the maximum continuous discontinuity ratio is defined as:
Figure BDA0002446803270000138
⑤ track composite quality
For the track quality evaluation, because the three track indexes have different importance degrees, different weights need to be allocated to the different track indexes, and the weights are respectively recorded as lambda1、λ2、λ3And λ1+λ2+λ 31. Define the integrated quality ζ of the flight path: ζ ═ λ1L+λ2Y+λ3D
And only when zeta is larger than or equal to kappa, the track is considered to have higher track comprehensive quality, and the next processing link can be entered, wherein kappa represents the evaluation threshold of the track comprehensive quality.
Step 5, integrating the flight paths and outputting a target comprehensive flight path
And aiming at the moment k, fusing all conventional tracking tracks output by a conventional tracking channel and all TBD tracks output by a TBD processing channel according to the following rules:
①, taking out any conventional tracking Track, wherein the measurement number of the conventional tracking Track in the interval [ k-N +1, k ] is P, searching all TBD tracks for tracks with the measurement overlap ratio of P/2 to the Track, and recording the number as N, wherein N is more than or equal to 0.
If n is 0, outputting the Track as a target integrated Track cut to the k-th frame;
if N is greater than 0, aiming at the time t, t ∈ [ k-N +1, k ], if the measurement of the time t on the Track is empty, the measurement points at the corresponding time on the N TBD tracks are searched for supplementation, if the number of the measurement points is more than 1, the point closest to the Track is selected for supplementation, if the number of the measurement points is 0, the supplementation is not carried out, and after the time t is traversed, the N TBD tracks are deleted.
② all conventional tracking tracks are traversed in sequence, as per the concept in ①.
③ all the conventional tracking tracks and the residual TBD tracks obtained after ① and ② processing are taken as target comprehensive tracks of the kth frame, sent to a conventional tracking processing module and enter the conventional tracking processing link of the (k + 1) th frame.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.

Claims (9)

  1. The method for combining the TBD based on double-threshold shunt processing and the conventional tracking in the MIMO radar is characterized by comprising the following steps:
    step 1, double-threshold detection
    The method comprises the steps of utilizing detection statistic data before the k-th frame threshold detection, and realizing the separation and parallel processing of a conventional tracking channel and a TBD processing channel by setting a normal detection threshold and a lower detection threshold lower than the normal detection threshold, wherein the conventional tracking channel corresponds to the normal detection threshold, and the TBD processing channel corresponds to the lower detection threshold;
    step 2, extracting trace point agglomeration and trace point information
    Respectively carrying out trace point condensation and trace point information extraction in a TBD processing channel and a conventional tracking channel;
    step 3, outputting the conventional tracking track
    In a conventional tracking channel, target tracking processing and first-class online track quality evaluation of a k-th frame are completed by utilizing a target comprehensive track formed by cutting to the k-1 th frame and trace point data of the k-th frame, an output track with quality passing through is called a conventional tracking track, meanwhile, trace point information which is associated with the target track in the k-th frame is transmitted to a TBD processing channel, and the trace point information is deleted from the TBD processing channel;
    step 4, outputting the TBD track
    In a TBD processing channel, performing sliding window type TBD processing by using point track data obtained by scanning from the kth-N +1 to the kth frame for N times to form a track segment with the length not greater than N; then, carrying out second type online track quality evaluation on the track segments, wherein the output track with over-quality is called TBD track;
    step 5, track fusion
    Performing track fusion on the TBD track and the conventional tracking track to form a target comprehensive track cut to the kth frame;
    step 6, circulation
    And entering a (k + 1) th frame, and repeating the steps from 1 to 5.
  2. 2. The method of claim 1, wherein the dual-threshold detection process comprises performing CFAR detection by using CFAR detection statistics respectively passing through a normal detection threshold and a lower detection threshold, and each CFAR detection statistic T is(r,θ)The formula for performing threshold detection is as follows:
    Figure FDA0002446803260000021
    wherein (r, θ) represents a distance-azimuth resolving unit,
    Figure FDA0002446803260000022
    indicating that there is a target hypothesis in the (r, theta) cell to be detected,
    Figure FDA0002446803260000023
    indicating no target hypothesis in the (r, theta) cell to be detected, and η indicating a detection threshold.
  3. 3. The method of claim 2, wherein η is determined by the false alarm probability required by the radar system in the conventional tracking channel, and the corresponding false alarm probability is set to 10-4Or 10-6；
    In the TBD processing path, η refers to the first threshold, and the corresponding false alarm probability is usually 10 according to the TBD algorithm requirement-1Or 10-2Magnitude.
  4. 4. The method for combining the conventional tracking and the TBD based on the dual-threshold shunt processing in the MIMO radar according to claim 1, wherein the trace point information extraction process comprises: and measuring the trace points from the same target, wherein the measurement information comprises the distance-azimuth position of the trace point centroid, the distance-azimuth span ratio of the trace points, the number of points in the trace points, the compactness of the trace points and the coincidence degree of the trace point centroid and the geometric center.
  5. 5. The method of claim 4, wherein the distance-azimuth position (r) of the centroid of the trace point is determined by combining the TBD based on the double-threshold splitting process with the conventional trackingCenter of mass,θCenter of mass)：
    Figure FDA0002446803260000024
    The distance-azimuth span ratio of the point trace, namely the ratio l of the distance span to the azimuth span of the point trace:
    Figure FDA0002446803260000025
    the number of points in the trace points, namely the number of threshold passing points belonging to the same target, is recorded as s;
    the compactness of the trace points reflects the position distribution condition of the trace points in the distance-azimuth span, and is defined as the product f of the number of the trace points and the distance-azimuth span:
    Figure FDA0002446803260000031
    the coincidence degree of the trace point centroid and the geometric center reflects the distribution condition of the trace point amplitude along with the position in the distance-azimuth span, and is defined as the Euclidean distance c between the trace point centroid and the geometric center:
    Figure FDA0002446803260000032
    wherein d isrRepresents the distance sampling interval, dθIndicating the angular separation between adjacent azimuth lines,
    Figure FDA0002446803260000033
    Figure FDA0002446803260000034
  6. 6. the method of claim 5, wherein the correlation degree of the measurement is calculated based on the point trace information extraction and measurement formation:
    (1) position of center of mass (r) measured according to time kCenter of mass,θCenter of mass) Judging whether the measurement falls into a candidate wave gate of the flight path, and if the measurement falls into the candidate wave gate, calling the measurement as candidate measurement;
    (2) and calculating the similarity of the target measurement at the k-1 moment and the candidate measurement at the k moment on four characteristic information including a distance-azimuth span ratio l, the number s of points in the point trace, the compactness degree f of the point trace and the coincidence degree c of the centroid of the point trace and the geometric center.
  7. 7. The method for combining the conventional tracking and the TBD based on the dual-threshold splitting processing in the MIMO radar according to claim 6, wherein the similarity in the step (2) is calculated by:
    ① the distance-azimuth span ratio of the m-th candidate measurement at the time k and the target measurement at the time k-1 is set as lm(k) And l (k-1), the similarity of the two measured distance-to-azimuth span ratios
    Figure FDA0002446803260000035
    Comprises the following steps:
    Figure FDA0002446803260000036
    ② points of the m-th candidate measurement at the time k and the target measurement at the time k-1 are respectively set as sm(k) And s (k-1), the similarity of the two measured points
    Figure FDA0002446803260000037
    Is composed of
    Figure FDA0002446803260000038
    ③ points of the m-th candidate measurement at the k moment and the target measurement at the k-1 moment are respectively set to be compact degreesm(k) And f (k-1), similarity of two measured trace compactness degrees
    Figure FDA0002446803260000041
    Comprises the following steps:
    Figure FDA0002446803260000042
    ④, c is the coincidence degree of the centroid and the geometric center of the m-th candidate measurement at the moment k and the target measurement at the moment k-1m(k) And c (k-1), similarity of coincidence degree of two measured mass centers and geometric center
    Figure FDA0002446803260000043
    Comprises the following steps:
    Figure FDA0002446803260000044
    finally, calculating the comprehensive association degree of the mth candidate measurement at the moment k and the target measurement at the moment k-1, and associating the candidate measurement with the maximum comprehensive association degree with the target measurement at the moment k-1;
    the overall correlation between the mth candidate measurement at time k and the target measurement at time k-1 is ξm(k)：
    Figure FDA0002446803260000045
    Wherein, α1、α2、α3、α4Respectively the similarity of the four characteristic information
    Figure FDA0002446803260000046
    And α, and1+α2+α3+α4＝1。
  8. 8. the method of claim 1, wherein the on-line track quality assessment comprises second threshold detection in the TBD processing, and the comprehensive quality of the track is assessed from track continuity, outlier number, and maximum continuous break duration.
  9. 9. The method for combining the conventional tracking and the TBD based on the dual-threshold shunt processing in the MIMO radar according to claim 1, wherein the step 5 specifically comprises:
    ①, taking out any conventional tracking Track, wherein the measurement number of the Track in the interval [ k-N +1, k ] is P, searching tracks with the measurement coincidence ratio of P/2 to the Track in all TBD tracks, and recording the number as N, wherein N is more than or equal to 0;
    if n is 0, outputting the Track as a target integrated Track cut to the k-th frame;
    if N is greater than 0, aiming at the time t, t ∈ [ k-N +1, k ], if the measurement of the time t on the Track is empty, the measurement points at the corresponding time on the N TBD tracks are searched for supplementation, if the number of the measurement points is more than 1, the point closest to the Track is selected for supplementation, if the number of the measurement points is 0, the supplementation is not carried out, and the N TBD tracks are deleted after the time t is traversed;
    ② following the steps in ①, traversing all conventional tracking tracks in turn;
    ③ all the conventional tracking tracks and the residual TBD tracks obtained after ① and ② processing are taken as target comprehensive tracks of the kth frame, sent to a conventional tracking processing module and enter the conventional tracking processing link of the (k + 1) th frame.
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