CN109031239A - Compressed sensing external illuminators-based radar based on information fusion is to object detection method - Google Patents
Compressed sensing external illuminators-based radar based on information fusion is to object detection method Download PDFInfo
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- CN109031239A CN109031239A CN201810992096.2A CN201810992096A CN109031239A CN 109031239 A CN109031239 A CN 109031239A CN 201810992096 A CN201810992096 A CN 201810992096A CN 109031239 A CN109031239 A CN 109031239A
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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
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Abstract
The invention belongs to signal processing technology field, it is related to a kind of compressed sensing based external illuminators-based radar to object detection method.Projection of the present invention first according to sparse signal in dictionary base obtains sparse vector, new calculation matrix is devised then in conjunction with gaussian random matrix and dictionary basic matrix, in the case of different for the time delay of echo under different paths, the different distribution character of compression sampling value is utilized and designs the weight coefficient after one group of optimization, and then proposes compressed sensing based information fusion detection method.Finally, completing the detection to target.This method is directly handled the sampled value of compression, is not necessarily to signal reconstruction, is reduced data operation quantity, can work normally under low signal-to-noise ratio.There is reference and practical application well in detection direction to be detected.
Description
Technical field
The invention belongs to signal processing technology field, it is related to a kind of compressed sensing based external illuminators-based radar and target is examined
Survey method.
Background technique
In recent years, the application that compressive sensing theory is reached its maturity in Radar Signal Processing, wireless communication, such as
Signal is detected, identification and parameter Estimation etc..It is different from traditional Nyquist sampling thheorem, it introduces after compressed sensing,
In the case where considerably reducing the analysis to sampled data output, the feature of the front signal of compression is not lost substantially.Radar at present
The compressed sensing technology used in system, it usually needs signal is reconstructed using restructing algorithm, and then realizes target detection
With parameter Estimation.But the general more demanding signal-to-noise ratio of restructing algorithm, it can just obtain meeting target detection and parameter Estimation
Reconstruction signal.For as external illuminators-based radar, since its essence is bistatic non-cooperation radar, target echo power is low, usually not
Meet the signal-to-noise ratio requirement of signal reconstruction.Therefore realize that Detection of Weak Signals needs new method using compressed sensing technology.
Signal detection be by receive signal analyze and determine echo signal whether there is, after compressed sensing technology,
The mathematical model of target detection is as follows,
Wherein Φ ∈ RM×NIt is calculation matrix, n ∈ RN×1It is white Gaussian noise, x ∈ RN×1It is signal to be detected, it is assumed that H0It is
Signal to be detected is not present, it is assumed that H1Signal to be detected there are the case where.Presently, there are detection algorithm mainly have it is following several
Kind method: (1) Liu Bing in 2010 et al. proposes mean value comparison algorithm, and core concept is that noise is the white noise that mean value is 0, that
H0In the case of E (y)=E (Φ n)=0, H1In the case of E (y)=E (Φ (x+n))=Φ x, and variance does not change in the case of two kinds
Become.So he use method be using actual sample value and its exist two kinds hypothesis in the case of mathematic expectaion deviation as adjudicate
Foundation completes detection;(2) 2015 years Alireza Hariri et al. propose maximum likelihood ratio value-based algorithm, and core concept is that do not have
There is the likelihood function for obtaining two kinds of situations to signal reconstruction to be detected, then does ratio, it is special by likelihood ratio function probability density
Property obtains thresholding formula, makes decisions.But above-mentioned three kinds of detection algorithms are that -5dB or less cannot realize target in signal-to-noise ratio
Detection.Which limits the application ranges of detector, in order to detect bigger range, it is necessary to which it is right under more low signal-to-noise ratio to solve
The detection of signal to be detected.Property (3) 2017 years Ma Junhu et al. fixed according to sparse vector non-zero position, proposes and is based on
The Weak Signal Detecting Arithmetic of sparse domain accumulation.The algorithm solves the problems, such as low signal-to-noise ratio.But this algorithm needs each group of number
According to sparse element position be identical.For distributed external illuminators-based radar scene, corresponding to each receiver
Time delay and doppler information are different from, then its is sparse for rarefaction representation under dictionary base for the target echo that generates of different receivers
Position is not also identical.The above-mentioned method failure based on element position accumulation.Therefore, on the basis of not increasing data volume, into one
Step research when sparse vector nonzero element position difference, the detection to signal be very it is necessary to.
Summary of the invention
The technical problem to be solved by the present invention is to study to work as sparse vector nonzero element position not simultaneously to compressed
Signal directly detects signal under conditions of no signal reconstruction.
In order to make it easy to understand, the technology used to the present invention is done as described below:
According to formula (1), following signal detection model is established
Wherein yn∈RM×1Indicate the signal n=1,2 of n-th compression sampling ..., Nb, nnFor white Gaussian noise, distribution
Are as follows:Sparse signal representation are as follows: xn=Ψ αn, Ψ ∈ RN×NIt is unit dictionary basic matrix.Sparse signal is in dictionary
Base projects to obtain sparse vector αn=ΨHxn,Nonzero element number much smaller than sparse
Vector element number, for different observation frequency αnIt is different to beTherefore sparse member cannot be used
The method of plain position accumulation.X is observed for singlen, when signal determines, sparse vector determines therewith, and non-zero position is corresponding
Numerical value is regarded as constant.Therefore, projection of the sparse vector under Gaussian matrix still Gaussian distributed.White Gaussian noise is dilute
Thin domain does not have sparsity, projects Gaussian distributed in dictionary base.And projection of the white Gaussian noise under random gaussian matrix
Also Gaussian distributed.The present invention combines the calculation matrix of random gaussian matrix and the design of dictionary basic matrix are as follows:
In H0Under signal detection model, ynDistribution are as follows:In H1Under detection model, ynDistribution
Are as follows:
It is identical to compare its mean value known to both the above Gaussian Profile, variance is different, therefore the detector is set according to the size of variance
Meter.But for multiple observed case, due to being set herein by the thought merged using information per signal-to-noise ratio difference all the way
Detector is counted, detection probability is effectively improved, specific method is one group of weight coefficient of design to fusion center, is optimized
Data afterwards design detector in turn, and concrete scheme is as shown in Figure 1.
In H0Under assuming that, this is distributed as standardized normal distribution:Then pass through NbSecondary sight
Survey ynVariance Tn=D (yn) mean value composition vector are as follows: m0=[1,1 ..., 1]T, further analysis obtains TnVariance are as follows:And in H1Under detection model:If ynVariance Tn's
The vector of mean value composition are as follows:WhereinAs shown in Figure 1, defining statisticWherein weight coefficientAndWhereinIt is
WithFor the diagonal matrix of diagonal entry composition.
To solve the above problems, the technical solution of the present invention is as follows:
A kind of compressed sensing based external illuminators-based radar is used for compressed signal object detection method, this method
Directly signal is detected under conditions of no signal reconstruction, which comprises the following steps:
S1, signal detection model is established:
Wherein yn∈RM×1Indicate the signal n=1,2 of n-th compression sampling ..., Nb, nnFor white Gaussian noise, distribution
Are as follows:Φ is Gaussian matrix, and Ψ is unit dictionary basic matrix, NbFor target echo number;
S2, it determines detection threshold, specifically includes:
S21, random gaussian matrix Φ and dictionary basic matrix Ψ is generated, designs calculation matrixN is Chief Signal Boatswain
Degree, σnFor the received noise power of n-th of receiver;
S22, calculation matrix ΦnNoise-containing signal compression is sampled to obtain yn=Φn(xn+nn), xnIt is letter to be detected
Number;
S23, y is soughtnVariance, be denoted as en;
S24, definitionWherein wnFor weight coefficient, andWherein,Be withFor the diagonal matrix of diagonal entry composition, m0=[1,1 ..., 1]TIt is H in step sl0Detection model
It is lower to pass through NbSecondary observation ynVariance mean value composition vector,It is H in step sl1Under detection model
ynVariance mean value composition vector;
S3, according to detector threshold value γ, whether true T > γ is judged, if so, being judged to signal;Otherwise it is judged to signal
It is not present, the value of γ is determined according to noise profile.
Beneficial effects of the present invention are to obtain sparse vector in the projection of dictionary base according to sparse signal first, then tie
It closes gaussian random matrix and dictionary basic matrix devises new calculation matrix, it is different for the time delay of echo under different paths
The case where, the different distribution character of compression sampling value is utilized and designs the weight coefficient after one group of optimization, and then proposes based on pressure
Contract the information fusion detection method perceived.Finally, completing the detection to target.This method is directly handled the sampled value of compression,
Without signal reconstruction, reduce data operation quantity, can be worked normally under low signal-to-noise ratio.Have in detection direction to be detected fine
Reference and practical application.
Detailed description of the invention
Fig. 1 the method for the present invention schematic diagram;
Fig. 2 the method for the present invention is to signal detection flow chart to be detected;
Fig. 3 is M/N=0.5 when compression ratio, the detection performance situation of change of detector when different false-alarm probabilities.
Specific embodiment
Below in conjunction with drawings and examples, technical solution of the present invention is further described.
Embodiment
The detection implementation method of embodiment is as shown in attached drawing 1, attached drawing 2.Shown in specific step is as follows:
Known signal to be detected is single frequency sinusoidal signal xn=An*exp(1j*2πfn(0:N-1)), wherein
For n-th of receiver received signal amplitude, SNRnFor signal-to-noise ratio, fn=[0.12 0.25 0.34 0.43 0.52 0.60], n
=1 ..., Nb, Nb=6, N=1000.Enable sparse dictionary base Ψ ∈ RN×NIt is unit Fourier transform matrix, Φn∈RM×NFor with
Machine Gaussian matrix, wherein M=500.nn∈RN×1For white Gaussian noise, the corresponding signal-to-noise ratio of each receiver is respectively SNRn=
[-5,-7,-9,-8.5,-6,-9.5]。
Step 1: generating random gaussian matrix Φ and dictionary basic matrix Ψ, designs calculation matrixN is signal
Length, σnFor the received noise power of n-th of receiver;
Step 2: calculation matrix ΦnNoise-containing signal compression is sampled to obtain yn=Φn(xn+nn), xnIt is to be detected
Signal;
Step 3: y is soughtnVariance, be denoted as en;
Step 4: definitionWherein wnFor weight coefficient, andWherein
m0=[1,1 ..., 1]T,
Step 5: if T > γ, signal has been judged to it;Otherwise signal is judged to be not present, wherein γ is detector threshold value,
It is determined according to noise profile.
Fig. 3 is the emulation to embodiment, is illustrated from Fig. 3 in compression ratio M/N=0.5, the inspection under different false-alarm probabilities
The detection performance situation for surveying device, as can be seen from Figure 3 when receiver signal-to-noise ratio is all lower, this method still can be with higher general
Rate detects target, and is not necessarily to signal reconstruction, therefore, can obtain of the invention with good detection performance.
Claims (1)
1. compressed sensing based external illuminators-based radar is to object detection method, this method is not for having compressed signal
Directly signal is detected under conditions of signal reconstruction, which comprises the following steps:
S1, signal detection model is established:
Wherein yn∈RM×1Indicate the signal n=1,2 of n-th compression sampling ..., Nb, nnFor white Gaussian noise, distribution are as follows:Φ is Gaussian matrix, and Ψ is unit dictionary basic matrix, NbFor target echo number;
S2, it determines statistic, specifically includes:
S21, random gaussian matrix Φ and dictionary basic matrix Ψ is generated, designs calculation matrixN is signal length, σn
For the received noise power of n-th of receiver;
S22, calculation matrix ΦnNoise-containing signal compression is sampled to obtain yn=Φn(xn+nn), xnIt is signal to be detected;
S23, y is soughtnVariance, be denoted as en;
S24, statistic is definedWherein wnFor weight coefficient, andWherein,Be withFor the diagonal matrix of diagonal entry composition, m0=[1,1 ..., 1]TIt is H in step sl0Detection model
It is lower to pass through NbSecondary observation ynVariance mean value composition vector,It is H in step sl1Under detection model
ynVariance mean value composition vector;
S3, according to detector threshold value γ, whether true T > γ is judged, if so, being judged to signal;Otherwise signal is judged to not deposit
It is determined in the value of, γ according to noise profile.
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CN106872951A (en) * | 2017-01-03 | 2017-06-20 | 北京环境特性研究所 | A kind of darkroom WB-RCS measuring method based on compressed sensing |
CN107884752A (en) * | 2017-11-08 | 2018-04-06 | 电子科技大学 | It is a kind of based on the external illuminators-based radar of compressed sensing to object detection method |
CN107976663A (en) * | 2018-01-24 | 2018-05-01 | 电子科技大学 | It is a kind of based on the external illuminators-based radar of subspace projection to targeted compression detection method |
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