CN113702965A - Improved accumulation method based on peak value convergence and simultaneous detection method of strong and weak targets - Google Patents

Improved accumulation method based on peak value convergence and simultaneous detection method of strong and weak targets Download PDF

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CN113702965A
CN113702965A CN202111013314.1A CN202111013314A CN113702965A CN 113702965 A CN113702965 A CN 113702965A CN 202111013314 A CN202111013314 A CN 202111013314A CN 113702965 A CN113702965 A CN 113702965A
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accumulation
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CN113702965B (en
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薄钧天
王国宏
于洪波
张翔宇
谭顺成
李林
张亮
温镇铭
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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
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • 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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter

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Abstract

The invention discloses an improved accumulation method based on peak value convergence and a method for simultaneously detecting strong and weak targets, belonging to the field of weak target detection. In the non-coherent accumulation based on Hough transformation, the method of the invention utilizes the energy accumulation result to improve the point accumulation result, and completes the simultaneous detection of the strong and weak targets. The method comprises the following steps: describing and normalizing a measuring point by adopting radial distance-time data, finishing point-to-curve conversion by adopting Hough conversion, and combining and accumulating point numbers and energy; secondly, improving a point accumulation result by using an energy accumulation result by adopting a peak value aggregation optimization method to form a new point accumulation result; thirdly, setting a threshold in the new point accumulation result, extracting a track formed by measuring points in a unit passing through the threshold, setting speed, course and acceleration constraint conditions, and outputting all tracks meeting the constraint conditions to finish target detection; the method has high probability of detecting a plurality of targets with different echo energies, and the engineering is easy to realize.

Description

Improved accumulation method based on peak value convergence and simultaneous detection method of strong and weak targets
Technical Field
The invention relates to the field of radar data processing, and is suitable for detecting all targets under the condition that a plurality of targets with large echo energy difference exist.
Background
Track-Before-Detect (TBD) is a technique for effectively detecting a weak target in a strong clutter environment, and can be used for detecting a weak target; the TBD algorithm enables the integral accumulation value of the flight path to be higher than that of the false flight path by accumulating multi-frame data, and the signal-to-clutter ratio is improved; the HT-TBD method based on Hough-Transform (HT) belongs to a TBD method based on projection Transform, has the characteristics of insensitivity to local defect and strong robustness to clutter noise, is firstly proposed to be applied to radar target detection in 1994 by B.D. Carlson, and provides a point accumulation mode to simultaneously detect targets with different echo energies, and then the HT-TBD technology is continuously improved.
However, when a plurality of targets exist in the detection range, the echo energy difference between the plurality of targets and the radar is large due to the fact that the radial distance difference between the plurality of targets and the radar is large; by adopting the existing HT-TBD algorithm, the peak clustering phenomenon exists in the energy accumulation space, the accumulation peak value of the strong target is high, the accumulation peak value of the weak target is low, the strong target submerges the weak target by simply setting a threshold, and on the other hand, a large number of peak values appear in the point accumulation space, so that the target is detected along with a large number of false tracks; therefore, how to improve the accumulation mode, avoid the strong target covering the weak target, and effectively detect a plurality of targets at the same time is an urgent problem to be solved.
The method comprises the steps of firstly utilizing Hough transformation to complete point-to-line transformation of measuring points, obtaining new point accumulation and energy accumulation results by only storing each measuring point in the unit with the maximum energy accumulation in the parameter unit of the measuring point by taking energy accumulation as reference after double accumulation is completed, setting a threshold according to the point accumulation results, extracting a target track, obtaining a final result after track constraint is carried out, and completing simultaneous detection of multiple targets.
Disclosure of Invention
The invention aims to solve the problem that a plurality of targets with different energy cannot be effectively detected in the existing pre-detection tracking technology so as to improve the detection and tracking capability of a radar on multiple targets; firstly, describing data by adopting a radial distance-time coordinate, reducing the influence of measurement errors, and normalizing the coordinate to enable 2 dimensions to be in the same order of magnitude; and then Hough transformation is carried out, the parameter units are divided to carry out point number accumulation and energy accumulation, the label matrix is adopted to record the parameter units through which curves of all measuring points pass, all measuring points are traversed, only the measuring points are stored in the parameter units which can form the maximum energy accumulation value, new point number accumulation and energy accumulation results are obtained, only thresholds are set in a point number accumulation space to extract target tracks, track constraint and combination are carried out, and final tracks are output.
The invention discloses an improved accumulation method and a simultaneous strong and weak target detection method based on peak value convergence, which comprise the following technical measures:
step one, adopting normalized radial distance-time description on the position coordinates of all received radar measuring points to reduce the influence of measuring errors, simultaneously keeping the order of magnitude of the coordinates of two dimensions close to prevent information loss, completing point-to-line conversion according to a Hough conversion rule, wherein the original points belong to a straight line, and the converted lines meet at one point; the method comprises the following specific steps:
the radar is located at the origin of coordinates on the two-dimensional Cartesian plane, the coordinates of the measuring point are (x, y), the radial distance of the measuring point is r, the time information is t, and then
Figure BDA0003239042290000021
After the radial distances of all measuring points are calculated, a coordinate normalization coefficient is set to be gamma, and when the actual radar detects a target, the radial distance value of the target is greater than the time information value, then:
Figure BDA0003239042290000022
wherein r ismaxMaximum radial distance value, t, for all coordinatesmaxIs the maximum time information value of all coordinates, | represents taking positive value, [ ·]Expressing rounding up, and the coordinate normalization mode is as follows:
(r,t)→(r,γ·t)
and carrying out Hough transformation on the normalized coordinates to map the normalized coordinates into a parameter space:
ρ＝r·cosθ+γ·k·sinθ
wherein rho represents the distance from a data space to an origin through a measuring point straight line, theta represents the included angle between a connecting line of the measuring point and the origin and the positive direction of a coordinate axis, and theta is in a pi/N mode from 0 to piθTaking values in order for the series of tolerance equidifferences, NθThe number of values of θ is shown.
Step two, completing double accumulation on a parameter plane; because of the existence of measurement errors, the flight path of a target in a data space is not a straight line in a strict sense, in order to realize the fault tolerance of the measurement errors in a parameter space, the parameter space is discretized, so that point trajectory curves from the same flight path can fall into the same unit for effective accumulation, and the rho-theta parameter space is divided into Nρ×NθUnit of, NρRepresenting the number of divisions of the dimension ρ, the side length of each cell is:
Figure BDA0003239042290000023
where ρ ismaxAnd ρminRespectively representing the maximum value and the minimum value of the distances from all straight lines where all the point traces are located to the original point in the data space;
setting Nρ×NθBinary accumulation matrix A ofpAnd energy accumulation matrix AeFor any normalized measurement point P0＝(r0,γ·k0) And when:
Figure BDA0003239042290000031
accumulating the two accumulation matrixes, wherein in the accumulation process, in order to fully utilize time information and more effectively detect the flight path, assuming that a plurality of curves pass through the unit k at the moment, only the maximum energy value is taken for accumulation:
Figure BDA0003239042290000032
Figure BDA0003239042290000033
Figure BDA0003239042290000034
wherein the content of the first and second substances,
Figure BDA0003239042290000035
respectively representing the number of points and energy newly accumulated by the parameter unit at the moment k, nkThe number of measurement points at time k is shown across the parameter element curve.
Step three, performing peak value aggregation by using the two completed accumulation results, so that each target is only stored in the parameter unit with the maximum energy; setting a data space to have N measuring points after passing through a first threshold, and setting omega as a cell group of each parameter unit storage measuring point after the accumulation:
Figure BDA0003239042290000036
with n measurement points in the parameter units (α, β):
Figure BDA0003239042290000037
a (alpha, beta) is a parameter unit (alpha, beta) memory point matrix, and radial distance, azimuth angle, energy and time information of a measuring point are stored; numbering all measurement points, and setting a label matrix LiAnd a final storage unit Gi，LiAll the parameter units used for marking the measurement points i are located, if the measurement points i coexist in the m parameter units, then:
Figure BDA0003239042290000041
let the measurement point number i equal to 1, so that the final storage unit
Figure BDA0003239042290000042
Extraction parameter unit
Figure BDA0003239042290000043
The new matrix composed of the measurement points with different times is set as
Figure BDA0003239042290000044
Figure BDA0003239042290000045
Then, let the number j be 2, set the parameter χ:
Figure BDA0003239042290000046
wherein [ ·]Representing rounding up, M representing the number of frames measured by the radar, and a current parameter unit
Figure BDA0003239042290000047
When the number of measurement points exceeds χ, the parameter unit is extracted
Figure BDA0003239042290000048
Composition of measurement points different from the moment of measurement point iNew matrix is set as
Figure BDA0003239042290000049
In the final storage unit GiAnd
Figure BDA00032390422900000410
finding the measuring points at the same time, and summing the energy values in respective matrixes to obtain results EsjAnd Esi(ii) a When parameter unit
Figure BDA00032390422900000411
When the number of the measurement points in the internal content does not exceed χ, it is determined whether j < m is true, if so, j is equal to j +1, and E is determinedsiAnd EsjThe measurement point i is kept only in the more energetic cell, if Esi＜EsjDelete now final storage unit GiAnd subtracting the attribute value represented by the measurement point i from the point accumulation and the energy accumulation:
Figure BDA00032390422900000412
then change the final storage unit
Figure BDA00032390422900000413
Let Esi＝Esj(ii) a If E issi＞EsjOnly deleting the unit
Figure BDA00032390422900000414
Measuring the point i, and subtracting the attribute value of the measuring point i from the point accumulation and the energy accumulation; and when j is less than m, judging whether i is less than N, if so, making i equal to i +1, otherwise, setting a threshold for the point number accumulation result, and extracting a peak value to output a track.
Step four, setting a threshold output track and carrying out track constraint; after peak value aggregation, flight path measuring points are aggregated into the same parameter unit, each measuring side point only exists in one unit, at the moment, a short-distance target accumulation value is still higher than a long-distance target accumulation value in an energy accumulation space, therefore, a threshold xi is only set in a new point accumulation space, the point path in the parameter unit with the accumulation value exceeding the threshold is extracted, and in order to realize missed detection fault tolerance of the measuring points in actual detection and avoid detecting more false flight paths, the threshold xi refers to 7/10 logic, namely:
Figure BDA00032390422900000415
the method is limited by conditions such as flight environment, power equipment and the like, a target follows certain physical condition limitation during flying, speed, course and acceleration constraint conditions are set, false flight paths are deleted, and measuring points at 3 moments are assumed to be arbitrarily selected in the same flight path
Figure BDA0003239042290000051
Figure BDA0003239042290000052
Setting a distance vector according to the time:
Figure BDA0003239042290000053
Figure BDA0003239042290000054
the upper limit and the lower limit of the target flight speed are respectively set as vmaxAnd vminThe maximum value of the target flight steering angle is phimaxThe maximum value of the target acceleration is amaxThen the target track should satisfy the constraint formula:
Figure BDA0003239042290000055
compared with the prior art, the improved accumulation method based on peak value convergence and the simultaneous detection method of the strong and weak targets have the advantages that:
1) the invention optimizes the point accumulation result by using the energy accumulation result, sets a threshold in the improved point accumulation result, eliminates the covering phenomenon of a strong target to a weak target, and effectively realizes the simultaneous detection of a plurality of targets with larger echo energy difference;
2) after the peak value is gathered, false tracks generated in the dense clutter range of actual detection are fewer, a plurality of targets can be detected more cleanly and effectively, and the detection effect is improved.
Drawings
FIG. 1 is a flow chart of an improved accumulation method and a simultaneous strong and weak target detection method based on peak value clustering;
FIG. 2 is an x-y plane radar map;
FIG. 3 is a normalized radial distance-time plane radar metrology map;
FIG. 4 is a graph of non-coherent accumulation results without modification;
FIG. 5 is a graph of non-coherent accumulation results after peak convergence;
FIG. 6 is a diagram of a track validation result;
FIG. 7 is the probability of detection results for different numbers of targets in an embodiment of the algorithm of the present invention;
FIG. 8 shows the probability of detection of different numbers of targets in the example of the conventional HT-TBD algorithm.
Detailed Description
3 targets are arranged on a two-dimensional plane, the radar position is taken as a 2-dimensional plane coordinate origin, the initial position of a target I is (240km,260km), the initial speed is (1500m/s,3000m/s), and the acceleration is (20 m/s)2,15m/s2) (ii) a The initial position of the second target is (320km,285km), the initial speed is (2000m/s, -150m/s), and the acceleration is (-10 m/s)2,30m/s2) (ii) a The initial position of target three is (300km,340km), the initial velocity is (2000m/s,1800m/s), the acceleration is (0 m/s)2,0m/s2) (ii) a Radar transmission power Pt2000W, antenna gain G t10, wavelength λ 0.1m, scanning periodThe period T is 1s, the distance measurement error is set to be 200m, and the azimuth angle measurement error is set to be 0.2 degrees; the invention is further described in detail with reference to the attached figure 1, and the processing flow of the invention is divided into the following steps with reference to the attached figure 1:
the method comprises the following steps: coordinate preprocessing
Environmental parameter setting clutter number obeying to each frame
Figure BDA0003239042290000065
The positions of the Poisson distribution are uniformly distributed, and the overall signal-to-noise ratio is 10dB for simulation verification; the radar measurement results obtained by collectively processing the M-10 frame data are shown in fig. 2, and normalization coefficients are calculated:
Figure BDA0003239042290000061
the normalization coefficient γ is obtained as 5079.4, and since the time information is much smaller in order of magnitude than the radial distance information, the coefficient is multiplied by the time information, and the mapped measurement point is shown in fig. 3.
Step two: parametric planar non-coherent accumulation
Hough transformation is carried out on r-t plane data, a parameter space is divided into 180 multiplied by 300 resolution units, and a point accumulation matrix A is setpAnd energy accumulation matrix AePerforming point accumulation and energy accumulation, equivalent weight measuring point (r)0,k0) Satisfies the following conditions:
Figure BDA0003239042290000062
a combined accumulation of votes and energy is carried out in units (α, β):
Figure BDA0003239042290000063
Figure BDA0003239042290000064
Figure BDA0003239042290000071
obtaining double accumulation of points and energy (fig. 4);
at the moment, the peak clustering phenomenon exists in both the point accumulation space and the energy accumulation space, and the short-distance target energy accumulation value is larger, so that the long-distance energy accumulation value can be submerged; however, it can be found that the accumulated value of the parameter unit with the target track is higher than the accumulated values of the surrounding parameter units, and each measurement point is only stored in the parameter unit with the maximum accumulated value by adopting a peak value aggregation optimization method, so as to obtain a new point number accumulated result and an energy accumulated result (fig. 5).
Step three: peak extraction and track correction
At the moment, the problem of parameter space peak clustering is effectively solved, although the phenomenon that a short-distance target submerges a long-distance target still exists in energy accumulation, the accumulated value of a parameter unit where a point accumulation space target is located is not large, different targets can be extracted only by setting a threshold in the point accumulation space, for M-10 frame accumulation, considering that the track truncation phenomenon can occur, setting a new point accumulation threshold to be 7, extracting a track formed by measuring points in a unit meeting the accumulation threshold, and then setting a speed constraint condition vminTaking Ma5, vmaxTaking Ma20, the heading constraint condition is set to phi due to the existence of the measurement errormax150 DEG, the acceleration constraint condition is set as amax＝50m/s2(ii) a The track should be satisfied
Figure BDA0003239042290000072
Resulting in the final output track (fig. 6).
Because the invention aims at improving the detection effect of multiple targets, the invention compares the detection probability of the number of the targets with the existing HT-TBD algorithm; suppose that in the q-th simulation, the ithThe track of the target is measured as IiThe j measuring point in the flight path is detected to have the coefficient of epsilonijAnd then:
Figure BDA0003239042290000073
setting the successful coefficient of the ith target track detection as sigmaqiWith logic 7/10, it is assumed that the track detection is successful when 7 measured points in the track are detected:
Figure BDA0003239042290000081
defining the coefficient gamma of all the targets on the q simulation plane with at least H targets being detectedq：
Figure BDA0003239042290000082
Then after the simulation is over, the probability that at least H targets are detected is
Figure BDA0003239042290000083
Figure BDA0003239042290000084
Obey the number of clutter per frame
Figure BDA0003239042290000085
The Poisson distribution is simulated under different overall signal-to-noise ratios, and the detection probability change curves (figure 7) that the algorithm can detect at least 1 target and can detect at least 2 targets and 3 targets are all detected are observed, the detection probability change curves (figure 8) that the algorithm can detect at least 1 target and can detect at least 2 targets and 3 targets are all detected are calculated in the same way without peak value aggregation.
In the embodiment, the detection probability of the target number is compared with that of the existing HT-TBD algorithm under the condition of different signal-to-noise ratios, and the result shows that the detection success rate of the invention for different numbers is obviously improved, and after the total signal-to-noise ratio reaches 2dB, the detection success rate for all targets reaches over 90 percent, which is far superior to that of the existing HT-TBD algorithm.

Claims (3)

1. An improved accumulation method based on peak value poly-optimization, which is characterized by comprising the following steps:
for measuring points received by a radar, after the normalized radial distance-time coordinate of the measuring points is subjected to Hough transformation, point and energy are respectively combined and accumulated on a formed curve to form a point accumulation result and an energy accumulation result;
after the energy accumulation and the point number accumulation of the measuring point are finished, the measuring point P after the coordinates are normalizediThe peak value is gathered, the curve changed by Hough transformation passes through m units on the parameter plane, and the coordinate is
Figure FDA0003239042280000011
The final storage unit is
Figure FDA0003239042280000012
Initially let j equal 1 and label matrix Li：
Figure FDA0003239042280000013
M is the radar measurement frame number, and the storage threshold is X:
Figure FDA0003239042280000014
recombination matrix
Figure FDA0003239042280000015
By unit
Figure FDA0003239042280000016
Is neutralized with PiComposition of measurement points at different times:
Figure FDA0003239042280000017
j goes from 2 to m when the cell
Figure FDA0003239042280000018
When the number of the internal measurement points exceeds x, a recombination matrix of the unit is formed
Figure FDA0003239042280000019
At GiAnd
Figure FDA00032390422800000110
finding the measuring points at the same time and respectively summing the energy to obtain the value EsiAnd Esj；
Judgment EsiAnd EsjSize of (E), if Esi＜EsjIn a
Figure FDA00032390422800000111
Deletion of P iniAnd accumulating points and energy by subtracting PiThe represented attribute value:
Figure FDA00032390422800000112
wherein A isp(Gi) Is a unit GiAccumulation of the number of points, Ae(Gi) Is a unit GiAccumulation of energy of GiBecome into
Figure FDA00032390422800000113
EsiIs changed to Esj；
If Esi＞EsjIn a
Figure FDA00032390422800000114
Deletion of P iniSubtracting P in point accumulation and energy accumulationiThe attribute value of (2);
and traversing the i from 1 to N to finish the peak value aggregation of each measuring point to obtain a new point number accumulation result and a new energy accumulation result, wherein N is the number of the measuring points after the first threshold is passed.
2. The method for simultaneously detecting strong and weak targets is characterized by further comprising the following steps after the improved accumulation method based on peak value aggregation as claimed in claim 1 is used:
setting a threshold xi in the new point accumulation result, extracting a flight path formed by measuring points in units passing through the threshold, and setting an upper limit v of the target flight speedmaxLower limit vminMaximum value of target flight steering angle phimaxMaximum value of target acceleration amaxCalculating the measuring points of 3 moments in the same track
Figure FDA0003239042280000021
Figure FDA0003239042280000022
Distance vector:
Figure FDA0003239042280000023
Figure FDA0003239042280000024
when the following constraint formula is satisfied:
Figure FDA0003239042280000025
and considering the flight path as a real flight path and outputting a result.
3. The method for simultaneously detecting strong and weak targets as claimed in claim 2, wherein:
the setting method of the threshold xi is as follows:
Figure FDA0003239042280000026
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