CN113702965B - Improved accumulation method based on peak value optimization and simultaneous detection method for strong and weak targets - Google Patents
Improved accumulation method based on peak value optimization and simultaneous detection method for strong and weak targets Download PDFInfo
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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
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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
- 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 optimization and a simultaneous detection method of strong and weak targets, and belongs to the field of weak target detection. In the method, in non-coherent accumulation based on Hough transformation, the point accumulation result is improved by using the energy accumulation result, and the simultaneous detection of the strong and weak targets is completed. The method comprises the following steps: describing and normalizing measuring points by adopting radial distance-time data, finishing the transformation from the points to the curves by adopting Hough transformation, and carrying out the combination and accumulation of the points and the energy; secondly, improving the point accumulation result by using the energy accumulation result by adopting a peak value optimizing method to form a new point accumulation result; setting a threshold in the new point accumulation result, extracting tracks 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 the targets with different echo energies, and is easy to realize engineering.
Description
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 larger echo energy differences exist.
Background
Tracking Before Detection (TBD) is a technology for effectively detecting a weak target in a strong clutter environment, and can be used for detecting the weak target; the TBD algorithm enables the integral accumulation value of the track to be higher than that of the false track by accumulating multi-frame data, and improves the signal-to-noise ratio; 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 defects and strong robustness to clutter noise, is first proposed by B.D. Carlson in 1994 to be applied to radar target detection, and provides a point accumulation mode to detect targets with different echo energies simultaneously, and then the HT-TBD technology is continuously improved.
However, when there are a plurality of targets within the detection range, the difference in echo energy of the plurality of targets is large due to the large difference in radial distances from the radar; by adopting the existing HT-TBD algorithm, the energy accumulation space has the phenomenon of peak cluster congestion, the accumulation peak value of a strong target is high, the accumulation peak value of a weak target is low, a method of simply setting a threshold can cause the strong target to submerge the weak target, and on the other hand, a large number of peaks 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 masking of weak targets by strong targets, and detect multiple targets simultaneously and effectively is a problem to be solved.
The method comprises the steps of firstly completing point-to-line conversion of measuring points by utilizing Hough conversion, taking energy accumulation as a reference after double accumulation is completed, storing each measuring point in only the unit with the largest energy accumulation in parameter units to obtain new point accumulation and energy accumulation results, setting a threshold according to the point accumulation results, extracting a target track, obtaining a final result after track constraint, and completing multi-target simultaneous detection.
Disclosure of Invention
The invention aims to improve the problem that a plurality of targets with different energy cannot be effectively detected in the prior tracking technology before detection so as to improve the detection tracking capability of a radar on multiple targets; firstly, describing data by adopting radial distance-time coordinates, reducing the influence of measurement errors, normalizing coordinates to enable 2 dimensions to be in the same magnitude; and then carrying out Hough transformation, dividing the parameter units to carry out point accumulation and energy accumulation, adopting a label matrix to record the parameter units passed by each measuring point curve, traversing all measuring points to store each measuring point in the parameter unit with the maximum energy accumulation value, obtaining new point accumulation and energy accumulation results, setting a threshold to extract a target track only in a point accumulation space, carrying out track constraint and combination, and outputting a final track.
The invention discloses an improved accumulation method based on peak value optimization and a simultaneous detection method of strong and weak targets, which comprise the following technical measures:
firstly, adopting normalized radial distance-time description to the received position coordinates of all radar measuring points so as to reduce the influence of measurement errors, simultaneously keeping the coordinates of two dimensions to be similar in order of magnitude, preventing information loss, completing point-to-line conversion according to Hough conversion rules, and intersecting the line after conversion with a point which belongs to a straight line originally; the method comprises the following specific steps:
the radar is positioned at the origin of coordinates on a two-dimensional Cartesian plane, the coordinates of the measuring points are (x, y), the radial distance is r, and the time information is t, then
After the radial distances of all measuring points are calculated, setting the coordinate normalization coefficient as gamma, and when the actual radar detects the target, if the radial distance value of the target is larger than the time information value, then:
wherein r is max For the maximum radial distance value, t, of all coordinates max For the maximum time information value of all coordinates, |·| represents taking a positive value, [ ·]The representation is rounded up, and the coordinate normalization mode is as follows:
(r,t)→(r,γ·t)
and carrying out Hough transformation mapping on the normalized coordinates into a parameter space:
ρ＝r·cosθ+γ·k·sinθ
wherein ρ represents the distance from the straight line of the measuring point to the origin in the data space, θ represents the included angle between the connecting line of the measuring point and the origin and the forward direction of the coordinate axis, and θ is pi/N from 0 to pi θ Sequentially taking values for the arithmetic series of the tolerance, N θ The number of values of θ is represented.
Step two, double accumulation is completed on the parameter plane; due to the existence of measurement errorsIn the method, the track of the target in the data space is not a straight line in the strict sense, the discretization processing is carried out on the parameter space to realize the fault tolerance of measurement errors in the parameter space, so that the point track curves from the same track can fall into the same unit to be effectively accumulated, and the rho-theta parameter space is divided into N ρ ×N θ Units N ρ Representing the number of divisions of dimension ρ, the side length of each cell is:
wherein ρ is max And ρ min Respectively representing the maximum value and the minimum value of the distances from all straight lines where all the traces are located to the original point in the data space;
setting N ρ ×N θ Binary accumulation matrix a of (2) p And an energy accumulation matrix A e For any normalized measuring point P 0 ＝(r 0 ,γ·k 0 ) When the following conditions are satisfied:
in the accumulating process, in order to make full use of time information, tracks are detected more effectively, and a plurality of curves pass through the unit k at the moment, and only the maximum energy value is used for accumulating:
wherein, the liquid crystal display device comprises a liquid crystal display device,
indicating the number of points and the energy needed to be newly accumulated by the k time parameter unit, n k The number of measurement points at the moment k is represented by the passing parameter unit curve.
Step three, peak value optimization is carried out by utilizing the two accumulation results, so that each target is only stored in the unit with the largest energy in the parameter units; let the data space pass through the first threshold and then have N measuring points, let Ω be the cell group of each parameter unit storage measuring point after the accumulation:
when n measuring points exist in the parameter unit (alpha, beta):
wherein A (alpha, beta) is a memory point matrix of a parameter unit (alpha, beta), and radial distance, azimuth angle, energy and time information of the measuring point are stored; numbering all measuring points, and setting a label matrix L i And a final storage unit G i ，L i For marking all parameter units where the measuring point i is located, if the measuring point i is set to coexist in m parameter units, then:
let measurement point number i=1, final storage unit
Extraction parameter Unit->
In measurements different from the moment of measurement point numbered iThe new matrix of dot composition is set to +.>
Let number j=2 then set parameter χ:
wherein [ (S)]Represents the upward rounding, M represents the radar measurement frame number, and when the parameter unit
When the number of the measurement points is more than χ, extracting the parameter unit +.>
The new matrix formed by measuring points with different moments from the measuring point i is set as
In the final storage unit G i And->
The measuring points at the same moment are searched, and the energy values are summed in respective matrixes, and the result is E respectively sj And E is si The method comprises the steps of carrying out a first treatment on the surface of the When parameter unit->
When the number of the measurement points contained in the memory is not more than χ, judging whether j < m is satisfied, if so, making j=j+1, and judging E si And E is connected with sj To retain the measuring point i only in the more energetic cells, provided E si ＜E sj Delete the final storage unit G at this time i Measuring point i in (a), and subtracting the genus represented by measuring point i from the point accumulation and the energy accumulationSex values:
and then changing the final storage unit
Make E si ＝E sj The method comprises the steps of carrying out a first treatment on the surface of the If E si ＞E sj Only the unit needs to be deleted
The measurement point i is measured, and the attribute value of the measurement point i is subtracted from the point accumulation and the energy accumulation; and if j is less than m, judging whether i is less than N, if so, enabling i=i+1, and if not, setting a threshold for the point number accumulation result, and extracting a peak output track.
Step four, setting a threshold output track and carrying out track constraint; through peak value optimization, track measuring points are gathered into the same parameter unit, each measuring side point only exists in one unit, at the moment, a close-range target accumulation value higher than a long-range target accumulation value still exists in an energy accumulation space, therefore, a threshold xi is only set in a new point accumulation space, and the parameter unit inner points with accumulation values exceeding the threshold are extracted, so that fault tolerance to missed detection of the measuring points in actual detection and detection of more false tracks are avoided, and the threshold xi refers to 7/10 logic, namely:
under the limitation of the conditions of flight environment, power equipment and the like, the target is subject to certain physical condition limitation during flight, speed, course and acceleration constraint conditions are set, false tracks are deleted, and measuring points at 3 moments are assumed to be taken in the same track
Setting a distance vector according to the time:
setting the upper limit and the lower limit of the target flying speed as v respectively max And v min The maximum value of the target flight steering angle is phi max The maximum value of the target acceleration is a max Then the target track should satisfy the constraint formula:
compared with the prior art, the improved accumulation method based on peak value aggregation and the simultaneous detection method for strong and weak targets have the beneficial effects that:
1) According to the invention, the point accumulation result is optimized by using the energy accumulation result, a threshold is set in the improved point accumulation result, the covering phenomenon of a strong target to a weak target is eliminated, and the simultaneous detection of a plurality of targets with larger echo energy differences is effectively realized;
2) After peak value optimization, the false tracks generated in the actual detection dense clutter range are fewer, so that 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 based on peak value optimization and a method for simultaneously detecting strong and weak targets;
FIG. 2 is an x-y plane radar measurement plot;
FIG. 3 is a normalized radial range-time plane radar survey plot;
FIG. 4 is a graph of non-coherent accumulation results without improvement;
FIG. 5 is a graph of non-coherent accumulation results after peak value aggregation;
FIG. 6 is a diagram of a track validation result;
FIG. 7 is a graph showing the probability of detection results for different numbers of targets in an embodiment of the algorithm of the present invention;
fig. 8 is a graph showing the probability of detection of different numbers of targets by the prior art HT-TBD algorithm in an embodiment.
Detailed Description
Setting 3 targets on a two-dimensional plane, taking the radar position as the coordinate origin of the 2-dimensional plane, wherein the initial position of the first target is 240km,260km, the initial speed is 1500m/s,3000m/s and the acceleration is 20m/s 2 ,15m/s 2 ) The method comprises the steps of carrying out a first treatment on the surface of the The initial position of the second target is 320km, 284 km, the initial speed is 2000m/s, -150m/s, and the acceleration is (-10 m/s) 2 ,30m/s 2 ) The method comprises the steps of carrying out a first treatment on the surface of the The initial position of the object three was (300 km,340 km), the initial velocity was (2000 m/s,1800 m/s), and the acceleration was (0 m/s 2 ,0m/s 2 ) The method comprises the steps of carrying out a first treatment on the surface of the Radar transmit power P t Antenna gain g=2000W t =10, wavelength λ=0.1m, scanning period t=1s, ranging error set to 200m, azimuth angle error set to 0.2 °; the invention will be described in further detail with reference to fig. 1 of the specification, and the process flow of the invention is divided into the following steps with reference to fig. 1 of the specification:
step one: coordinate preprocessing
The ambient parameter sets the clutter quantity subject to each frame
The positions of the Poisson distribution are subjected to uniform distribution, and simulation verification is carried out on the condition that the overall signal-to-noise ratio is SCR=10dB; processing m=10 frames of data in a centralized manner to obtain radar measurement results, and calculating normalization coefficients as shown in fig. 2:
the normalized coefficient γ= 5079.4 is obtained, and since the order of magnitude of the time information is much smaller than the radial distance information, the coefficient is multiplied by the time information, and the mapped measurement point is given in fig. 3.
Step two: parameter plane non-coherent accumulation
Performing Hough transformation on r-t plane data, dividing a parameter space into 180×300 resolution units, and setting a point accumulation matrix A p And an energy accumulation matrix A e Performing point number accumulation and energy accumulation, and measuring equivalent measurement points (r 0 ,k 0 ) The method meets the following conditions:
the combined accumulation of ticket number and energy is performed in units (α, β):
obtaining a double accumulation of points and energy (fig. 4);
at this time, no matter the point accumulation space or the energy accumulation space has the phenomenon of peak cluster congestion, 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 value of the surrounding parameter units, and each measuring point is only stored in the parameter unit with the largest accumulated value by adopting a peak value optimizing method, so as to obtain a new point accumulated result and an energy accumulated result (fig. 5).
Step three: peak extraction and track correction
At this time, the parameter space peak cluster congestion problemThe method effectively solves the problem that although energy accumulation still has the phenomenon that a short-distance target floods a long-distance target, the accumulation values of parameter units where the point accumulation space targets are located are not different, different targets can be extracted only by setting a threshold in the point accumulation space, for M=10 frames accumulation, considering that a track cut-off phenomenon can occur, setting a new point accumulation threshold as 7, extracting tracks formed by measuring points in units meeting the accumulation threshold, and setting a speed constraint condition v min Taking Ma5, v max Taking Ma20, the heading constraint is set to phi due to the existence of measurement errors max =150°, the acceleration constraint is set to a max ＝50m/s 2 The method comprises the steps of carrying out a first treatment on the surface of the The track should satisfy
Resulting in a final output track (fig. 6).
Because the invention aims to improve the detection effect of multiple targets, the invention is compared with the existing HT-TBD algorithm through the detection probability of the number of targets; assume that in the q-th simulation, the track measurement of the ith target is I i The jth measuring point in the track is detected to have a coefficient epsilon ij Then:
let the i-th target track detection success coefficient be sigma qi With 7/10 logic, it is considered that track detection is successful when 7 measurement points in the track are detected:
defining the coefficient that all the targets on the plane in the q-th simulation are detected by at least H targets as gamma q ：
After the simulation is finished, the probability that at least H targets are detected is
Let the clutter number per frame obey
By simulating the poisson distribution under different overall signal-to-noise ratios, observing the detection probability change curves (figure 7) that the algorithm of the invention can detect at least 1 target, at least 2 targets and 3 targets, and at least 1 target, at least 2 targets and 3 targets, can be detected without peak value optimization by adopting the same method (figure 8).
In the embodiment, the detection probability comparison of the target number under different signal-to-noise ratio conditions is carried out by the method and the conventional HT-TBD algorithm, and the result shows that the method has obvious improvement on the detection success rate of different numbers, and the detection success rate of all targets reaches more than 90% after the overall signal-to-noise ratio reaches 2dB, which is far superior to the conventional HT-TBD algorithm.
Claims (3)
1. An improved accumulation method based on peak aggregation, characterized by comprising the steps of:
for a measuring point received by a radar, after normalized radial distance-time coordinates are subjected to Hough transformation, respectively carrying out point number and energy combination accumulation on the formed curve to form a point number accumulation result and an energy accumulation result;
after the energy accumulation and the point accumulation of the measuring point are completed, the measuring point P with normalized coordinates is measured i Peak value optimization is carried out, the curve which is changed by Hough transformation passes through m units on a parameter plane, and sitting is carried outMarked as
The final storage unit is->
Initial j=1, label matrix L i ：
M is the radar measurement frame number, and the storage point threshold is χ:
recombination matrix
By units->
Intermediate and P i The measuring points with different moments are composed of:
j traverses from 2 to m, when the parameter unit
When the number of the measurement points is more than χ, extracting the parameter unit +.>
The new matrix of measuring point compositions different from the moment of measuring point i is set as +.>
In the final storage unit G i And->
The measuring points at the same moment are searched, and the energy values are summed in respective matrixes, and the result is E respectively sj And E is si The method comprises the steps of carrying out a first treatment on the surface of the When parameter unit
Judging whether j < m is true when the number of the measurement points contained in the measurement points is not more than χ, if yes, enabling j=j+1 to continue traversing, otherwise stopping traversing;
judgment E si And E is connected with sj If E is of the size of si ＜E sj In the following
Delete P in i And subtracting P from the point accumulation and the energy accumulation i Representative attribute value:
wherein A is p (G i ) As unit G i Accumulation of points under A e (G i ) As unit G i The energy accumulation under the condition, gi becomes
Esi is changed to Esj;
if E si ＞E sj In the following
Delete P in i Subtracting P in point accumulation and energy accumulation i Attribute values of (2);
i traversing from 1 to N, finishing peak value optimization of each measuring point, and obtaining a new point accumulation result and a new energy accumulation result, wherein N is the number of measuring points after passing through a first threshold.
2. The method for simultaneously detecting strong and weak targets is characterized by further comprising the following steps after using the improved accumulation method based on peak value optimization as set forth in claim 1:
setting a threshold xi in the new point accumulation result, extracting a track formed by measuring points in a unit passing through the threshold, and setting an upper limit v of the target flying speed max Lower limit v min Maximum value of target flight steering angle
Maximum value of target acceleration a max Calculating measuring points +.3 times in the same track>
Distance vector:
the following constraint formula is satisfied:
the track is considered to be a real track and the result is output.
3. The simultaneous detection method of strong and weak targets according to claim 2, wherein: threshold xi setting methodThe method comprises the following steps:
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