CN103076601B - Clutter intensity divided self-adaptive dynamic target detection - Google Patents

Clutter intensity divided self-adaptive dynamic target detection Download PDF

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CN103076601B
CN103076601B CN201210590840.9A CN201210590840A CN103076601B CN 103076601 B CN103076601 B CN 103076601B CN 201210590840 A CN201210590840 A CN 201210590840A CN 103076601 B CN103076601 B CN 103076601B
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袁子乔
罗丁利
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Xian Electronic Engineering Research Institute
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Abstract

The invention relates to clutter intensity divided self-adaptive dynamic target detection. An M times echo data of pulse Doppler radar in a coherent processing interval is rearranged, the arranged data is divided into primary and secondary channels, wherein the auxiliary channel adopts an FFT (Fast Fourier Transform Algorithm) to process, and an auxiliary channel clutter map is established as the basis of the selection of the primary channel fir filter bank weight coefficient; the primary channel is realized by adopting the fir filter bank; and the number of the weight coefficients of each filter is 4 which respectively correspond to different ground clutter and weather clutter situations, and one of the four weight coefficients is selected as the fir filter weight coefficient of the primary channel according to the auxiliary channel clutter map. The self-adaptive dynamic target detection provided by the invention has the advantages that the primary and secondary channels are adopted to process, the auxiliary channel is processed by adopting FFT, and the primary channel is realized by adopting the fir filter bank; and the auxiliary channel clutter map is established to indicate the current clutter background of the radar, and the weight coefficient of the primary channel fir filter is selected according to the clutter intensity.

Description

The strong and weak self-adaptation moving-target of dividing of a kind of clutter detects
Technical field
The invention belongs to the target detection technique field in Radar Signal Processing, be specifically related to the strong and weak self-adaptation moving-target of dividing of a kind of clutter and detect.
Background technology
Target detection is the requisite ingredient of Radar Signal Processing System.The moving target detection method of traditional pulse Doppler radar, mainly comprises the such two kinds of methods of weighting FFT and MTI+ weighting FFT, and its disposal route operand is little, is easy to realize.Use weighting fft algorithm, in order to obtain larger land clutter improvement factor, no matter be also Shi Fei clutter district of clutter district, all need to be weighted, most window functions that adopt are realized, and signal to noise ratio (S/N ratio) generally has the loss of 1.5dB left and right.And for the method for MTI+FFT, the method that adopts multiple-pulse to offset, although can suppress preferably the impact of land clutter, can cause the response of friction speed passage to occur difference, affect asking for of the true Doppler of target.
M the radar return data that are input as pulse Doppler radar that moving-target detects, after data rearrangement by the M of a same distance unit data, through the M rank wave filter of M different frequency response, obtain M data output, respectively corresponding different Doppler's passages.Use FFT can realize the M rank wave filter of M different frequency response simultaneously, and if adopt bank of filters to realize, need the individual independently M rank complex filter of M.
Summary of the invention
The technical matters solving
For fear of the deficiencies in the prior art part, the present invention proposes the strong and weak self-adaptation moving-target of dividing of a kind of clutter and detects, and can be applied to the dual-use pulse Doppler radar product scope that carries out moving-target detection under clutter background.
Technical scheme
The strong and weak self-adaptation moving-target of dividing of a kind of clutter detects, and it is characterized in that step is as follows:
Step 1: M echo data in relevant processing interval of paired pulses radar Doppler, carries out data ordering and obtains data rearrangement S cp, described S cpcomprise the capable N row of a M data, M is echo times, and N is range unit number;
Step 2: to data S cp, to classify unit as, adopt M point FFT to process, and ask for mould value, obtain the range Doppler panel data X of accessory channel f; Described X fcomprise M dxN data, M dfor Doppler's passage, on numerical value, equate with echo times M; Choose Doppler's passage 1,2,3,4, M d-2, M d-1, M das ground clutter passage, residue Doppler passage is meteorological clutter passage;
Step 3: the maximal value of choosing in the ground clutter channel data on same distance unit is concerned with and processes interval accessory channel ground clutter data Xd as this n+1, by Xd n+1with ground clutter diagram data Yd nbe weighted summation: Yd n+1=Xd n+1× k d+ Yd n× (1-k d), obtain new ground clutter diagram data Yd n+1; Wherein weighting coefficient k dfor ground clutter, figure upgrades coefficient;
The maximal value of choosing in the meteorological clutter channel data on same distance unit is concerned with and processes interval accessory channel meteorological clutter data Xq as this n+1, by Xq n+1with meteorological clutter diagram data Yq nbe weighted summation: Yq n+1=Xq n+1× k q+ Yq n× (1-k q), obtain new meteorological clutter diagram data Yq n+1; Wherein weighting coefficient k qfor Meteorological Clutter Map upgrades coefficient;
Step 4: ground clutter and meteorological clutter grade are judged:
By the ground clutter diagram data Yd on any distance unit n+1(i) divided by radar ground noise A noise, according to ratio, ground clutter is divided into Three Estate:
Without ground clutter: if ratio is in interval [0,5], look on this range unit without ground clutter;
Weak ground clutter: if ratio interval (5,100], look and on this range unit, have weak ground clutter;
Strong surface feature clutter: if ratio is in interval (100 ,+∞), depending on there is strong surface feature clutter on this range unit;
By the meteorological clutter diagram data Yq on any distance unit n+1(i) divided by radar ground noise A noise, according to ratio, meteorological clutter is divided into Three Estate:
Without meteorological clutter: if ratio is in interval [0,5], look on this range unit without meteorological clutter;
Weak meteorological clutter: if ratio interval (5,100], look and on this range unit, have weak meteorological clutter;
Strong meteorological clutter: if ratio is in interval (100 ,+∞), depending on there is strong meteorological clutter on this range unit;
Described radar ground noise A noiseelectrical noise mean value while start for radar;
Step 5: the ground clutter obtaining according to step 4 and meteorological clutter grade, to data rearrangement S cptake the filtering adapting to process to realize the detection of self-adaptation moving-target:
Situation 1: if during without ground clutter with without meteorological clutter situation, adopt the wave filter weight coefficient of M not windowing, to data rearrangement S cpcarry out fir filtering processing, realize self-adaptation moving-target and detect;
Situation 2: if without ground clutter and weak meteorological clutter or situation 3: weak ground clutter and weak meteorological clutter or situation 4: when strong surface feature clutter and weak meteorological clutter, adopt 30dB Chebyshev's windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, can realize self-adaptation moving-target and detect;
Situation 5: if without ground clutter and strong meteorological clutter or situation 6: weak ground clutter and strong meteorological clutter or situation 7: when strong surface feature clutter and strong meteorological clutter, adopt 45dB Chebyshev's windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, realize self-adaptation moving-target and detect;
Situation 8: if weak ground clutter and without meteorological clutter or situation 9: strong surface feature clutter and during without meteorological clutter, adopts not windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, realize self-adaptation moving-target and detect.
Described M is 2 n power.
Described weighting coefficient k dcodomain be (0,0.125].
Described weighting coefficient k qcodomain be (0,0.25].
Beneficial effect
The strong and weak self-adaptation moving-target of dividing of a kind of clutter that the present invention proposes detects, under different clutter backgrounds, the solution that the problem of clutter improvement factor and detectability contradiction proposes, relevant M echo data of processing in interval of pulse Doppler radar is reset, the data after resetting are divided into major-minor two passages.Wherein accessory channel adopts FFT to process, and sets up accessory channel clutter map, the foundation of selecting as main channel fir bank of filters weight coefficient; Main channel adopts fir bank of filters to realize, the M rank weight coefficient of each wave filter is precalculated and be stored in the ROM in sheet, the weight coefficient of each wave filter comprises 4, corresponding different land clutter and meteorological clutter situations respectively, and according to accessory channel clutter map, select one of 4 weight coefficients as main channel fir wave filter weight coefficient.
Compared with classic method, superiority of the present invention is
1, use major-minor two passages to process, accessory channel adopts FFT to process, and main channel adopts fir bank of filters to realize;
2, set up accessory channel clutter map, the current clutter background of indication radar, and according to clutter power, select the weight coefficient of main channel fir wave filter.
Accompanying drawing explanation
Fig. 1: the frequency response curve of the bank of filters in the present invention when situation 1;
Fig. 2: the frequency response curve of the bank of filters while in the present invention being situation 2 or situation 3 or situation 4;
Fig. 3: the frequency response curve of the bank of filters while in the present invention being situation 5 or situation 6 or situation 7;
Fig. 4: the frequency response curve of the bank of filters while being situation 8 or situation 9 in the present invention.
Embodiment
Now in conjunction with the embodiments, the invention will be further described for accompanying drawing:
Step 1:
M echo data in relevant processing interval of paired pulses radar Doppler, carries out data ordering and obtains data rearrangement S cp, described S cpcomprise the capable N row of a M data, M is echo times, and N is range unit number; Described M is 2 n power;
Step 2:
Step 1: to data S cp, to classify unit as, adopt M point FFT to process, and ask for mould value, obtain the range Doppler panel data X of accessory channel f; Described X fcomprise M dxN data, M dfor Doppler's passage, on numerical value, equate with echo times M; Choose Doppler's passage 1,2,3,4, M d-2, M d-1, M das ground clutter passage, residue Doppler passage is meteorological clutter passage;
Step 2:
The maximal value of choosing in the ground clutter channel data on same distance unit is concerned with and processes interval accessory channel ground clutter data Xd as this n+1, by Xd n+1with ground clutter diagram data Yd nbe weighted summation, produce new ground clutter diagram data Yd n+1, computing formula is: Yd n+1=Xd n+1× k d+ Yd n× (1-k d), wherein weighting coefficient k dfor ground clutter, figure upgrades coefficient, codomain be (0,0.125];
The maximal value of choosing in the meteorological clutter channel data on same distance unit is concerned with and processes interval accessory channel meteorological clutter data Xq as this n+1, by Xq n+1with meteorological clutter diagram data Yq nbe weighted summation, produce new meteorological clutter diagram data Yq n+1, computing formula is: Yq n+1=Xq n+1× k q+ Yq n× (1-k q), wherein weighting coefficient k qfor Meteorological Clutter Map upgrade coefficient, codomain be (0,0.25];
Step 3:
By the ground clutter diagram data Yd on any distance unit n+1(i) divided by radar ground noise A noiseif ratio, in interval [0,5], is looked on this range unit without ground clutter; If ratio interval (5,100], look and on this range unit, have weak ground clutter; If ratio is in interval (100 ,+∞), depending on there is strong surface feature clutter on this range unit; Described radar ground noise A noiseelectrical noise mean value while start for radar;
By the meteorological clutter diagram data Yq on any distance unit n+1(i) divided by radar ground noise A noiseif ratio, in interval [0,5], is looked on this range unit without meteorological clutter; If ratio interval (5,100], look and on this range unit, have weak meteorological clutter; If ratio is in interval (100 ,+∞), depending on there is strong meteorological clutter on this range unit; Described radar ground noise A noiseelectrical noise mean value while start for radar;
Step 3:
If range unit i is upper for without ground clutter with without the clutter distribution situation of meteorological clutter, use the wave filter weight coefficient of M not windowing, to data rearrangement S cpcarry out fir filtering processing, can realize self-adaptation moving-target and detect.
Be the clutter distribution situation without ground clutter and weak meteorological clutter or weak ground clutter and weak meteorological clutter or strong surface feature clutter and weak meteorological clutter if range unit i is upper, use 30dB Chebyshev windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, can realize self-adaptation moving-target and detect.
Be the clutter distribution situation without ground clutter and strong meteorological clutter or weak ground clutter and strong meteorological clutter or strong surface feature clutter and strong meteorological clutter if range unit i is upper, use 45dB Chebyshev windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, can realize self-adaptation moving-target and detect.
If range unit i is upper for weak ground clutter with without meteorological clutter or strong surface feature clutter with without the clutter distribution situation of meteorological clutter, uses not windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, can realize self-adaptation moving-target and detect.
As shown in Fig. 1～4, be respectively the frequency response curve of four groups of bank of filters.

Claims (4)

1. the strong and weak auto-adaptive moving target detection method of dividing of clutter, is characterized in that step is as follows:
Step 1: M echo data in relevant processing interval of paired pulses radar Doppler, carries out data ordering and obtains data rearrangement S cp, described S cpcomprise the capable N row of a M data, M is echo times, and N is range unit number;
Step 2: to data S cp, to classify unit as, adopt M point FFT to process, and ask for mould value, obtain the range Doppler panel data X of accessory channel f; Described X fcomprise M d× N data, M dfor Doppler's passage, on numerical value, equate with echo times M; Choose Doppler's passage 1,2,3,4, M d-2, M d-1, M das ground clutter passage, residue Doppler passage is meteorological clutter passage;
Step 3: the maximal value of choosing in the ground clutter channel data on same distance unit is concerned with and processes interval accessory channel ground clutter data Xd as this n+1, by Xd n+1with ground clutter diagram data Yd nbe weighted summation: Yd n+1=Xd n+1× k d+ Yd n× (1-k d), obtain new ground clutter diagram data Yd n+1; Wherein weighting coefficient k dfor ground clutter, figure upgrades coefficient;
The maximal value of choosing in the meteorological clutter channel data on same distance unit is concerned with and processes interval accessory channel meteorological clutter data Xq as this n+1, by Xq n+1with meteorological clutter diagram data Yq nbe weighted summation: Yq n+1=Xq n+1× k q+ Yq n× (1-k q), obtain new meteorological clutter diagram data Yq n+1; Wherein weighting coefficient k qfor Meteorological Clutter Map upgrades coefficient;
Step 4: ground clutter and meteorological clutter grade are judged:
By the ground clutter diagram data Yd on any distance unit n+1(i) divided by radar ground noise A noise, i represents range unit, according to ratio, ground clutter is divided into Three Estate:
Without ground clutter: if ratio is in interval [0,5], look on this range unit without ground clutter;
Weak ground clutter: if ratio interval (5,100], look and on this range unit, have weak ground clutter;
Strong surface feature clutter: if ratio is in interval (100 ,+∞), depending on there is strong surface feature clutter on this range unit;
By the meteorological clutter diagram data Yq on any distance unit n+1(i) divided by radar ground noise A noise, i represents range unit, according to ratio, meteorological clutter is divided into Three Estate:
Without meteorological clutter: if ratio is in interval [0,5], look on this range unit without meteorological clutter;
Weak meteorological clutter: if ratio interval (5,100], look and on this range unit, have weak meteorological clutter;
Strong meteorological clutter: if ratio is in interval (100 ,+∞), depending on there is strong meteorological clutter on this range unit;
Described radar ground noise A noiseelectrical noise mean value while start for radar;
Step 5: the ground clutter obtaining according to step 4 and meteorological clutter grade, to data rearrangement S cptake the filtering adapting to process to realize the detection of self-adaptation moving-target:
Situation 1: if during without ground clutter with without meteorological clutter situation, adopt the wave filter weight coefficient of M not windowing, to data rearrangement S cpcarry out fir filtering processing, realize self-adaptation moving-target and detect;
Situation 2: if without ground clutter and weak meteorological clutter or situation 3: weak ground clutter and weak meteorological clutter or situation 4: when strong surface feature clutter and weak meteorological clutter, adopt 30dB Chebyshev's windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, can realize self-adaptation moving-target and detect;
Situation 5: if without ground clutter and strong meteorological clutter or situation 6: weak ground clutter and strong meteorological clutter or situation 7: when strong surface feature clutter and strong meteorological clutter, adopt 45dB Chebyshev's windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, realize self-adaptation moving-target and detect;
Situation 8: if weak ground clutter and without meteorological clutter or situation 9: strong surface feature clutter and during without meteorological clutter, adopts not windowing and have the wave filter weight coefficient of recess at normalized frequency 0 place, to data rearrangement S cpcarry out fir filtering processing, realize self-adaptation moving-target and detect.
2. the auto-adaptive moving target detection method that clutter power is divided according to claim 1, is characterized in that: k the power that described M is 2.
3. the auto-adaptive moving target detection method that clutter power is divided according to claim 1, is characterized in that: described weighting coefficient k dcodomain be (0,0.125].
4. the auto-adaptive moving target detection method that clutter power is divided according to claim 1, is characterized in that: described weighting coefficient k qcodomain be (0,0.25].
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CN106899279B (en) * 2017-01-24 2020-08-25 西安电子科技大学 Comprehensive moving target detection filter design method
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