CN106707273B - Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method - Google Patents
Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method Download PDFInfo
- Publication number
- CN106707273B CN106707273B CN201710050308.0A CN201710050308A CN106707273B CN 106707273 B CN106707273 B CN 106707273B CN 201710050308 A CN201710050308 A CN 201710050308A CN 106707273 B CN106707273 B CN 106707273B
- Authority
- CN
- China
- Prior art keywords
- signal
- quantization
- radar station
- radar
- probability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- 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
- G01S13/87—Combinations of radar systems, e.g. primary radar and secondary radar
-
- 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
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/04—Systems determining presence of a target
-
- 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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
-
- 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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
- G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
- G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
-
- 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
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—Extracting wanted echo-signals
Abstract
The invention belongs to Radar Technology field, disclose it is a kind of based on how the multistation Radar Signal Fusion detection method of graceful Pearson criterion quantization, comprising: setting netted radar system includes a signal fused inspection center and N number of radar station;Determine that each radar station to the quantization digit b of the echo-signal received, obtains quantized interval number M=2b;Setting signal merges the false-alarm probability of inspection center, determines the quantization threshold of N number of radar station;N number of radar station quantifies echo-signal according to corresponding quantization threshold, and label corresponding after quantization is transmitted to signal fused inspection center;Corresponding label after signal fused inspection center quantifies echo-signal according to false-alarm probability, N number of radar station, detects target;It reduces radar station echo-signal and is transmitted to communication bandwidth required when signal fused center.
Description
Technical field
The present invention relates to Radar Technology field, more particularly to it is a kind of based on how graceful Pearson came (Neyman-Pearson) criterion
The multistation Radar Signal Fusion detection method of quantization can be used for reducing radar and signal fused in the detection of multistation Radar Signal Fusion
Communication bandwidth between center.
Background technique
Multistation radar fence is made of a signal fused center and multiple radar stations.Compared with single radar, multistation radar
Net has many advantages, such as bigger investigative range, higher positioning accuracy, stronger survival ability and stronger anti-interference ability.Cause
This, the research of multistation radar fence becomes a new research hotspot.In the research of multistation radar fence, the signal of multistation radar
Fusion detection method is an important research direction.
For current existing research, the signal fused detection method of multistation radar is broadly divided into three kinds: multistation radar
The signal grade fusion detection method for adjudicating level fusing method and multistation radar of track level fusing method, multistation radar.Multistation thunder
The track level fusing method reached refers to that the track of oneself is transmitted to signal fused center by each radar station, and signal fused center is by institute
There is the Track Fusion of radar station at a track.The judgement level fusing method of multistation radar refers to each radar station by respective judgement
As a result it is transmitted to signal fused center, signal fused center finally judges having for target according to the court verdict of all radars
Nothing.The signal grade fusion detection method of multistation radar refers to that the echo-signal of oneself is transmitted to fusion center by each radar station, letter
Number fusion center judges the presence or absence of target according to all radar station echo-signals.
In detection performance, the fusion method of multistation radar adjudicated level fusing method and be better than track grade, and multistation radar
Signal grade fusion detection method be better than again adjudicate level fusing method.Currently, the track level fusing method of multistation radar at
It is ripe, and it has been applied to actual engineering system.The goal in research of next step is that signal level fusing method is applied to new one
The netted radar system in generation.For the signal grade fusion detection method of multistation radar, since each radar station needs will be all
Echo-signal is transmitted to signal fused center, it is therefore desirable to big communication bandwidth.But between radar station and signal fused center
Communication bandwidth it is limited, so required communication band when must study new quantization method to reduce radar echo signal transmission
It is wide.And while reducing communication bandwidth, guarantee the detection performance that signal fused center has had.It is returned for different radars
Wave signal model, it has been proposed that a variety of quantization criterion and corresponding Quantitative fusion detection algorithm.For example, in gaussian signal model
Under, quantified using maximizing Fisher information as criterion;Under Rayleigh signal model, with maximize Pasteur distance be criterion into
Row quantization etc..But the loss of existing multistation radar Quantitative fusion detection algorithm detection performance is larger, it is therefore desirable to further grind
Study carefully.
Summary of the invention
It is an object of the invention to be directed to the deficiency of above-mentioned prior art, propose a kind of based on how graceful Pearson criterion quantifies
Multistation Radar Signal Fusion detection method, to guarantee under conditions of given false-alarm probability, to maximize signal fused center
Detection probability be criterion quantify radar station echo-signal, reduce radar station echo-signal be transmitted to signal fused center when institute
The communication bandwidth needed.
In order to achieve the above objectives, the present invention is realised by adopting the following technical scheme.
It is a kind of based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method, which is characterized in that it is described
Method includes the following steps:
Step 1, netted radar system is set, and the netted radar system includes a signal fused inspection center and N number of
Radar station;
Step 2, determine that each radar station is obtained to the quantization digit b of the echo-signal received, and according to quantization digit b
Quantized interval number M=2b, the corresponding label of each quantized interval is expressed as m, m=0,1,2 ..., M-1, the quantization digit b
Meet: 1≤b < Ch/fs, wherein ChMaximum communication bandwidth between each radar station and signal fused inspection center, fsIt is every
The highest sample frequency of a radar station;
Step 3, the false-alarm probability of Setting signal fusion inspection center, according to the false-alarm probability and the quantized interval
Number determines the quantization threshold of N number of radar station;
Step 4, N number of radar station quantifies echo-signal according to corresponding quantization threshold, and will be right after quantization
The label answered is transmitted to signal fused inspection center;
Step 5, the signal fused inspection center is according to the false-alarm probability, N number of radar station to the echo-signal amount of progress
Corresponding label, detects target after change.
Advantages of the present invention is as follows: the present invention due under conditions of given false-alarm probability directly to maximize signal fused
The detection probability at center is that criterion quantifies radar echo signal, therefore, compared with the conventional method, in the condition of same communication bandwidth
Under, signal fused center can be made to obtain preferably detection property.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 is provided in an embodiment of the present invention a kind of based on how the multistation Radar Signal Fusion of graceful Pearson criterion quantization is examined
The implementation process schematic diagram of survey method;
Fig. 2 is the implementation process schematic diagram when present invention solves quantization threshold using majorized function;
It the method for the present invention that Fig. 3 is quantization digit when being 1 and maximizes detection probability of the Pasteur apart from quantization method and compares and show
It is intended to;
It the method for the present invention that Fig. 4 is quantization digit when being 2 and maximizes detection probability of the Pasteur apart from quantization method and compares and show
It is intended to;
It the method for the present invention that Fig. 5 is quantization digit when being 3 and maximizes detection probability of the Pasteur apart from quantization method and compares and show
It is intended to;
Fig. 6 Fig. 3 be quantization digit be 4 when the method for the present invention and maximize Pasteur apart from quantization method and non-quantized letter
The detection probability contrast schematic diagram of number fusion detection method.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the present invention provide it is a kind of based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method,
As shown in Figure 1, described method includes following steps:
Step 1, netted radar system is set, and the netted radar system includes a signal fused inspection center and N number of
Radar station.
It include a signal fused center and N number of thunder in netted radar system specifically, giving a netted radar system
Up to station;N number of radar station is denoted as the 1st to n-th radar station, permitted maximum between each radar station and signal fused center
Communication bandwidth is all identical;The maximum communication bandwidth allowed between radar station and signal fused center is denoted as Ch, maximum communication band
Wide ChUnit be bps;Highest sample frequency in N number of radar station is denoted as fs, highest sample frequency fsUnit be conspicuous
Hereby.
Step 2, determine each radar station to the quantization digit b (quantization of each radar station of the echo-signal received
Number is identical), and quantized interval number M=2 is obtained according to quantization digit bb, the corresponding label of each quantized interval is expressed as m, m
=0,1,2 ..., M-1, the quantization digit b meet: 1≤b < Ch/fs, wherein ChIt is detected for each radar station and signal fused
Maximum communication bandwidth between center, fsFor the highest sample frequency of each radar station.
On the basis of meeting above formula, the value of quantization digit b is bigger, and the detection performance at signal fused center is better;But
It is, when quantization digit b increases to a certain extent, to further increase quantization digit b and change to the detection performance at signal fused center
Kind very little, therefore, the experience value of quantization digit b are 3~5.
Step 3, the false-alarm probability of Setting signal fusion inspection center, according to the false-alarm probability and the quantized interval
Number determines the quantization threshold of N number of radar station.
Step 3 specifically includes following sub-step:
(3a) is denoted as x for N number of radar station, by the echo-signal that i-th of radar station receivesi, the noise of echo-signal
Than being denoted as λi, the test statistics of echo-signal is denoted as yi=xi/μi, wherein μiFor the noise power of i-th of radar station, i=1,
2,…,N;
In practice, if signal-to-noise ratio λiWith noise power μiTrue value it is unknown, it can be obtained by the method for estimation
Value.
M-1 quantization threshold of i-th of radar station is successively denoted as T by (3b)i,1,Ti,2,…,Ti,M-1, then i-th of radar station
The corresponding prior probability expression formula of m-th of quantized interval are as follows:
Wherein, viIndicate the test statistics y for the echo-signal that i-th of radar station receivesiCorresponding label after quantization,
H1It indicates to assume that target exists, H0It indicates to assume no target, P () indicates that probability value, exp indicate exponential function, P (vi=m |
H1) indicate under the conditions of assuming that target is existing, the test statistics y for the echo-signal that i-th of radar station receivesiIt is right after quantization
The label v answerediThe probability of=m, P (vi=m | H0) indicate assume it is aimless under the conditions of, i-th of radar station receive return
The test statistics y of wave signaliCorresponding label v after quantizationiThe probability of=m;
(3c) enables quantized interval corresponding label m=0,1,2 ..., M-1, to obtain the M quantization of i-th of radar station
The corresponding prior probability expression formula in section;
I=1,2 ..., N are enabled, to obtain the prior probability expression formula of the corresponding M quantized interval of N number of radar station;
(3d) obtains signal fused inspection according to the prior probability expression formula of the corresponding M quantized interval of N number of radar station
Expression formula L (the v of the likelihood ratio of measured center1,v2,…,vN) are as follows:
Wherein, m=0,1,2 ..., M-1, for any one radar station, the corresponding prior probability expression of quantized interval
Formula has M kind possibility, so that the likelihood ratio of signal fused inspection center has MNKind possibility, ∏ indicate even to multiply symbol;
(3e) is according to the likelihood ratio expression formula of the signal fused inspection center, the false-alarm of the signal fused inspection center
Likelihood ratio threshold value t and the corresponding probability value α of likelihood ratio threshold value is calculated in probability:
Wherein,Expression is more than likelihood ratio threshold value t to the likelihood ratio for meeting signal fused inspection center
All combinations sum,Indicate the likelihood to signal fused inspection center is met
Than being more than all combinations of likelihood ratio threshold value t assuming that the probability under no goal condition is summed.
(3f) is according to the likelihood ratio, the likelihood ratio threshold value and the likelihood ratio thresholding of the signal fused inspection center
It is worth corresponding probability value, obtains the detection probability P of signal fused inspection centerd:
(3g) is constructed using the detection probability for maximizing the signal fused inspection center as criterion and is solved N number of radar station point
Not corresponding M-1 quantization threshold Ti,1,Ti,2,…,Ti,M-1Optimized model:
s.t.0<Ti,1<…<Ti,M-1, i=1 ..., N
Wherein, f (Pd) it is detection probability PdFunction with guarantee minimize function f (Pd) be equivalent to maximize detection probability
Pd;
(3h) solves the Optimized model, obtains the corresponding M-1 quantization threshold T of N number of radar stationi,1,Ti,2,…,
Ti,M-1, i=1,2 ..., N.
Specifically, the Optimized model in step (3g) is the nonlinear programming problem of belt restraining, it is able to use existing non-
Linear optimization algorithm solves;Since the fminimax function in MATLAB software can quickly and effectively solve the problem,
This example solves to obtain the quantization threshold T of N number of radar station using the fminimax function in MATLAB softwarei,1,Ti,2,…,
Ti,M-1, i=1,2 ..., N.
As shown in Fig. 2, in sub-step (3g), according to quantization threshold Ti,1,Ti,2,…,Ti,M-1, the optimization of i=1,2 ..., N
Model solution obtains the quantization threshold T of N number of radar stationi,1,Ti,2,…,Ti,M-1, the detailed process of i=1,2 ..., N are as follows:
Cycle-index N is arranged in (3g1)c, the interim storage matrix T of N row M-1 column is set, minimum target function is set
Value fmin=1, setting cycle-index marks k=1;
Random quantization threshold initial value is arranged in (3g2)I=1,2 ..., N;
(3g3) is according to quantization threshold initial valueI=1,2 ..., N, using in MATLAB software
Fminimax function solves to obtain the quantization threshold value of this circulation according to the Optimized model in step (3g)
I=1,2 ..., the N and target function value f after minimumk;
(3g4) if minimize after target function value fk<fmin, then interim storage matrix is enabled
Minimum target functional value fmin=fk;
(3g5) is if cycle-index marks k < Nc, then the value of cycle-index label k adds 1, and goes to step (3g2);It is no
Then, step (3g6) is gone to;
The quantization threshold T of (3g6) according to interim storage matrix T, after being solvedi,1=T (i, 1), Ti,2=T (i,
2) ..., Ti,M-1=T (i, M-1), i=1,2 ..., N;T (i, j) indicates the element of the i-th row jth column of interim storage matrix T.
Illustratively, in sub-step (3g), building solves the corresponding M-1 quantization threshold T of N number of radar stationi,1,
Ti,2,…,Ti,M-1Optimized model:
s.t.0<Ti,1<…<Ti,M-1, i=1 ..., N
Wherein, f (Pd) it is detection probability PdFunction with guarantee minimize function f (Pd) be equivalent to maximize detection probability
Pd, function f (Pd) expression formula be f (Pd)=1-PdOr f (Pd)=1/Pd。
Step 4, N number of radar station quantifies echo-signal according to corresponding quantization threshold, and will be right after quantization
The label answered is transmitted to signal fused inspection center.
Step 4 specifically includes following sub-step:
(4a) i-th of radar station is according to its corresponding M-1 amountChange doorLimit Ti,1,Ti,2,…,Ti,M-1, i-th of radar station is connect
The test statistics y of the echo-signal receivediQuantified;If test statistics yiMeet Ti,m≤yi<Ti,m+1, then system is examined
Measure yiLabel v after quantizationi=m;
(4b) i-th of radar station is by test statistics yiLabel v after quantizationiIt is transmitted to signal fused inspection center;
(4c) enables i=1,2 ..., N, so that N number of radar station is by the label v after quantization1,v2,…,vNIt is transmitted to signal fused
Inspection center.
Step 5, the signal fused inspection center is according to the false-alarm probability, N number of radar station to the echo-signal amount of progress
Corresponding label, detects target after change.
Step 5 specifically includes following sub-step:
(5a) according to quantized interval number M and the corresponding label m=0 of quantized interval, 1,2 ..., M-1 obtain signal fused
All possible value v of the N number of label summation of inspection centersum={ 0,1,2 ..., N × (M-1) }, wherein × indicate multiplication sign;
Assuming that signal fused inspection center label summation value is v when no targetsumProbability be P (vsum|H0);
(5b) is according to the false-alarm probability Pfa, it is solved to obtain detection threshold g and probability value γ by following formula:
Wherein,Indicate to meet label summation value vsumAll labels more than detection threshold g
Sum value vsumAssuming that the probability under no goal condition is summed,It indicates to ask to meeting label
With value vsumAll labels summation value v equal to detection threshold gsumAssuming that the probability under no goal condition is asked
With.
(5c) signal fused inspection center is by the label v of the N number of radar station received1,v2,…,vNSummation, obtains label
The sum ofIf the sum of label vf> g, then judgement has target;If the sum of label vf=g, then with probability value γ judgement
There is target;If the sum of label vf< g then adjudicates no target.
Effect of the invention is further illustrated by the test of following simulation comparison:
1. simulation parameter be arranged: in netted radar system include 1 signal fused inspection center and 5 radar stations, this 5
The echo-signal of radar station signal-to-noise ratio having the same, the value range of signal-to-noise ratio are 0dB to 20dB, the value interval of signal-to-noise ratio
For 0.5dB;Quantization digit b=4, i.e. quantized interval number M=16;The false-alarm probability of signal fused inspection center is set as 10-6;
When solving the quantization threshold of each radar station, cycle-index N is setc=20.What this example and maximization Pasteur's distance quantified
Signal fused detection method compares.
2. emulation content:
Quantization digit b=1 is set, i.e. quantized interval number M=2 is arranged according to simulation parameter, calculates signal-to-noise ratio value model
The corresponding detection probability curve of interior the method for the present invention is enclosed as shown in the square marks line in Fig. 3;It gives and maximizes Pasteur's distance
Detection probability curve when quantization is as shown in the addition marks line in Fig. 3.
Quantization digit b=2 is set, i.e. quantized interval number M=4 is arranged according to simulation parameter, calculates signal-to-noise ratio value model
The corresponding detection probability curve of interior the method for the present invention is enclosed as shown in the square marks line in Fig. 4;It gives and maximizes Pasteur's distance
Detection probability curve when quantization is as shown in the addition marks line in Fig. 4.
Quantization digit b=3 is set, i.e. quantized interval number M=8 is arranged according to simulation parameter, calculates signal-to-noise ratio value model
The corresponding detection probability curve of interior the method for the present invention is enclosed as shown in the square marks line in Fig. 5;It gives and maximizes Pasteur's distance
Detection probability curve when quantization is as shown in the addition marks line in Fig. 5.
Quantization digit b=4 is set, i.e. quantized interval number M=16 is arranged according to simulation parameter, calculates signal-to-noise ratio value
The corresponding detection probability curve of the method for the present invention is as shown in the square marks line in Fig. 6 in range;Give maximize Pasteur away from
Shown in addition marks line of the detection probability curve such as in Fig. 6 when from quantization;In order to compare, the detection given when not quantifying is general
Rate curve is as shown in the circular mark line in Fig. 6.
3. analysis of simulation result:
By Fig. 3 to Fig. 6 it is found that giving identical quantization digit, detected with the signal fused for maximizing Pasteur's distance quantization
Method is compared, and the method for the present invention can obtain better detection performance.In the case where detection probability is 0.5, with maximization bar
The signal fused detection method of family name's distance quantization is compared, and when quantization digit is 1, the detection performance of this method is mentioned as shown in Figure 3
Rise 2.081dB;When quantization digit is 2, the detection performance of this method promotes 0.723dB as shown in Figure 4;When quantization digit is 3
When, the detection performance of this method promotes 0.306dB as shown in Figure 5;When quantization digit is 4, the detection of this method as shown in Figure 6
Performance boost 0.106dB.
In addition, it will be appreciated from fig. 6 that when quantization digit be 4 when, the detection probability curve of the method for the present invention with do not quantify when inspection
It is very close to survey probability curve, in the case where detection probability is 0.5, the detection performance loss of the method for the present invention only has
0.051dB。
In summary emulation experiment can be seen that the present invention according to how graceful Pearson criterion quantifies signal, and existing
Some is compared based on the quantization method for maximizing Pasteur's distance, under identical quantization digit, can obtain preferably detection property
Energy.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (4)
1. it is a kind of based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method, which is characterized in that the side
Method includes the following steps:
Step 1, netted radar system is set, and the netted radar system includes a signal fused inspection center and N number of radar
It stands;
Step 2, it determines that each radar station is respectively b to the quantization digit of the echo-signal received, and is obtained according to quantization digit b
To quantized interval number M, M=2b, the corresponding label of each quantized interval is expressed as m, m=0,1,2 ..., M-1;Institute
State quantization digit b satisfaction: 1≤b < Ch/fs;Wherein, ChMaximum between each radar station and signal fused inspection center is logical
Believe bandwidth, fsFor the highest sample frequency of each radar station;
Step 3, the false-alarm probability of Setting signal fusion inspection center, it is true according to the false-alarm probability and the quantized interval number
Fixed N number of corresponding quantization threshold of radar station;
Wherein, step 3 specifically includes following sub-step:
The echo-signal that i-th of radar station receives is denoted as x respectively for N number of radar station by (3a)i, i-th of radar station is connect
The signal-to-noise ratio of the echo-signal received is denoted as λi, the test statistics for the echo-signal that i-th of radar station receives is denoted as yi=
xi/μi;Wherein, μiFor the noise power of i-th of radar station, i=1,2 ..., N;
M-1 quantization threshold of i-th of radar station is successively denoted as T by (3b)I, 1, TI, 2..., TI, m..., TI, M-1, then i-th of thunder
The corresponding prior probability expression formula of m-th of quantized interval up to station are as follows:
Wherein, viIndicate the test statistics y for the echo-signal that i-th of radar station receivesiCorresponding label, H after quantization1Table
Show and assumes that target exists, H0It indicates to assume no target, P () indicates that probability value, exp indicate exponential function;P(vi=m | H1)
It indicates under the conditions of assuming that target is existing, the test statistics y for the echo-signal that i-th of radar station receivesiIt is corresponding after quantization
Label viThe probability of=m;P(vi=m | H0) indicate to assume it is aimless under the conditions of, echo that i-th of radar station receives
The test statistics y of signaliCorresponding label v after quantizationiThe probability of=m;M=0,1,2 ..., M-1;
(3c) enables quantized interval corresponding label m=0,1,2 ..., M-1, to obtain the M quantized interval of i-th of radar station
Corresponding prior probability expression formula;
I=1,2 ..., N are enabled, to obtain the prior probability expression formula of the corresponding M quantized interval of N number of radar station;
(3d) is obtained in signal fused detection according to the prior probability expression formula of the corresponding M quantized interval of N number of radar station
Expression formula L (the v of the likelihood ratio of the heart1, v2..., vN) are as follows:
Wherein, m=0,1,2 ..., M-1, for any one radar station, the corresponding prior probability expression formula of quantized interval has M
Kind possibility, so that the likelihood ratio of signal fused inspection center has MNKind possibility, П indicate even to multiply symbol;
(3e) is general according to likelihood ratio expression formula, the false-alarm of the signal fused inspection center of the signal fused inspection center
Rate calculates separately to obtain likelihood ratio threshold value t and the corresponding probability value α of likelihood ratio threshold value:
Wherein,Expression is more than the institute of likelihood ratio threshold value t to the likelihood ratio for meeting signal fused inspection center
There is combination to sum;
(3f) is according to the likelihood ratio, the likelihood ratio threshold value and the likelihood ratio threshold value pair of the signal fused inspection center
The probability value answered obtains the detection probability P of signal fused inspection centerd:
(3g) it is right respectively to construct the N number of radar station of solution using the detection probability for maximizing the signal fused inspection center as criterion
The M-1 quantization threshold T answeredI, 1, TI, 2..., TI, M-1Optimized model:
S.t.0 < TI, 1< ... < TI, M-1, i=1 ..., N
Wherein, f (Pd) it is detection probability PdFunction with guarantee minimize function f (Pd) be equivalent to maximize detection probability Pd;
(3h) solves the Optimized model, obtains the corresponding M-1 quantization threshold T of N number of radar stationI, 1, TI, 2..., TI, M-1,
I=1,2 ..., N;
Step 4, N number of radar station quantifies the echo-signal being respectively received according to corresponding quantization threshold respectively,
And label corresponding after quantization is transmitted separately to signal fused inspection center;
Step 5, after the signal fused inspection center quantifies echo-signal according to the false-alarm probability, N number of radar station
Corresponding label, detects target, and then knows in echo-signal that N number of radar station receives with the presence or absence of target.
2. it is according to claim 1 it is a kind of based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection side
Method, which is characterized in that in sub-step (3g), building solves the corresponding M-1 quantization threshold T of N number of radar stationI, 1,
TI, 2..., TI, M-1Optimized model:
S.t.0 < TI, 1< ... < TI, M-1, i=1 ..., N
Wherein, f (Pd) it is detection probability PdFunction, and minimize function f (Pd) be equivalent to maximize detection probability Pd, function f
(Pd) expression formula be f (Pd)=1-PdOr f (Pd)=1/Pd。
3. it is according to claim 1 it is a kind of based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection side
Method, which is characterized in that step 4 specifically includes following sub-step:
(4a) i-th of radar station is according to its corresponding M-1 quantization threshold TI, 1, TI, 2..., TI, M-1, i-th of radar station is received
The test statistics y of the echo-signal arrivediQuantified;If test statistics yiMeet TI, m≤yi< TI, m+1, then system is examined
Measure yiLabel v after quantizationi=m;
(4b) i-th of radar station is by test statistics yiLabel v after quantizationiIt is transmitted to signal fused inspection center;
(4c) enables i=1,2 ..., N, so that N number of radar station is by the label v after quantization1, v2..., vNIt is transmitted to signal fused detection
Center.
4. it is according to claim 1 it is a kind of based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection side
Method, which is characterized in that step 5 specifically includes following sub-step:
(5a) obtains signal fused detection according to quantized interval number M and quantized interval corresponding label m=0,1,2 ..., M-1
All possible value v of the N number of label summation in centersum={ 0,1,2 ..., N × (M-1) };Wherein, × indicate multiplication sign;
Assuming that signal fused inspection center label summation value is v when no targetsumProbability be P (vsum|H0);
(5b) is according to the false-alarm probability Pfa, it is solved to obtain detection threshold g and probability value γ by following formula:
Wherein,Indicate to meet label summation value vsumAll labels summation more than detection threshold g
Value vsumAssuming that the probability under no goal condition is summed,Expression takes to label summation is met
Value vsumAll labels summation value v equal to detection threshold gsumAssuming that the probability under no goal condition is summed;
(5c) signal fused inspection center is by the label v of the N number of radar station received1, v2..., vNSummation, obtains the sum of labelIf the sum of label vf> g, then adjudicating in the echo-signal that N number of radar station receives has target;If label it
And vf=g, then being adjudicated in the echo-signal that N number of radar station receives with probability value γ has target;If the sum of label vf< g, then
Adjudicating in the echo-signal that N number of radar station receives does not have target.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710050308.0A CN106707273B (en) | 2017-01-23 | 2017-01-23 | Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710050308.0A CN106707273B (en) | 2017-01-23 | 2017-01-23 | Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106707273A CN106707273A (en) | 2017-05-24 |
CN106707273B true CN106707273B (en) | 2019-05-21 |
Family
ID=58908860
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201710050308.0A Active CN106707273B (en) | 2017-01-23 | 2017-01-23 | Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106707273B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11984964B2 (en) | 2022-05-10 | 2024-05-14 | Raytheon Company | Decentralized control via adaptive importance encoding |
Families Citing this family (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107607926B (en) * | 2017-10-31 | 2020-07-03 | 西安电子科技大学 | Method for detecting and processing low-traffic quasi-signal fusion target of distributed radar |
CN108039940B (en) * | 2017-11-24 | 2020-12-11 | 天津大学 | Invalid CCE (control channel element) removing method for PDCCH (physical downlink control channel) of LTE (long term evolution) system |
TWI709188B (en) * | 2018-09-27 | 2020-11-01 | 財團法人工業技術研究院 | Fusion-based classifier, classification method, and classification system |
CN111812591B (en) * | 2020-06-16 | 2022-10-21 | 南京航空航天大学 | Target detection method based on Bayesian estimation |
CN113434816B (en) * | 2020-09-21 | 2023-06-06 | 重庆工商大学 | Method for detecting signal under noise enhancement Nawman-Pearson criterion |
CN113406583B (en) * | 2021-06-22 | 2022-08-02 | 电子科技大学长三角研究院(衢州) | Approximate calculation method for cloud MIMO radar target detection probability |
CN113534096B (en) * | 2021-07-19 | 2023-09-15 | 东莞理工学院 | LiDAR signal geometric feature extraction method and system based on spline function |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7079072B1 (en) * | 1987-01-23 | 2006-07-18 | Raytheon Company | Helicopter recognition radar processor |
CN102298140A (en) * | 2011-05-16 | 2011-12-28 | 中国人民解放军海军航空工程学院 | Radar fence anti-interference usefulness assessment method based on detection probability geometric distribution curve |
KR101546421B1 (en) * | 2015-02-16 | 2015-08-24 | 에스티엑스엔진 주식회사 | Adaptive constant false alarm rate processing method |
CN105425223A (en) * | 2015-11-11 | 2016-03-23 | 西安电子科技大学 | Detection method of sparse distance extension radar target in generalized Pareto clutter |
CN105954739A (en) * | 2016-04-20 | 2016-09-21 | 电子科技大学 | Knowledge-aided nonparametric constant false alarm detection method |
CN106199588A (en) * | 2016-06-24 | 2016-12-07 | 西安电子科技大学 | The multistation Radar Signal Fusion detection method quantified based on Pasteur's distance |
-
2017
- 2017-01-23 CN CN201710050308.0A patent/CN106707273B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7079072B1 (en) * | 1987-01-23 | 2006-07-18 | Raytheon Company | Helicopter recognition radar processor |
CN102298140A (en) * | 2011-05-16 | 2011-12-28 | 中国人民解放军海军航空工程学院 | Radar fence anti-interference usefulness assessment method based on detection probability geometric distribution curve |
KR101546421B1 (en) * | 2015-02-16 | 2015-08-24 | 에스티엑스엔진 주식회사 | Adaptive constant false alarm rate processing method |
CN105425223A (en) * | 2015-11-11 | 2016-03-23 | 西安电子科技大学 | Detection method of sparse distance extension radar target in generalized Pareto clutter |
CN105954739A (en) * | 2016-04-20 | 2016-09-21 | 电子科技大学 | Knowledge-aided nonparametric constant false alarm detection method |
CN106199588A (en) * | 2016-06-24 | 2016-12-07 | 西安电子科技大学 | The multistation Radar Signal Fusion detection method quantified based on Pasteur's distance |
Non-Patent Citations (1)
Title |
---|
分布式MIMO 雷达信号级量化融合检测方法研究;彭志刚等;《现代雷达》;20160831;第38卷(第8期);27 |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11984964B2 (en) | 2022-05-10 | 2024-05-14 | Raytheon Company | Decentralized control via adaptive importance encoding |
Also Published As
Publication number | Publication date |
---|---|
CN106707273A (en) | 2017-05-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106707273B (en) | Based on how graceful Pearson criterion quantization multistation Radar Signal Fusion detection method | |
Lee et al. | Deep cooperative sensing: Cooperative spectrum sensing based on convolutional neural networks | |
Shankar | Statistical models for fading and shadowed fading channels in wireless systems: A pedagogical perspective | |
CN106199588B (en) | Multistation Radar Signal Fusion detection method based on Pasteur's distance quantization | |
CN107290741A (en) | Combine the indoor human body gesture recognition method apart from time-frequency conversion based on weighting | |
CN101815305B (en) | Credibility based cooperative spectrum sensing method | |
CN107180260A (en) | Short wave communication frequency selecting method based on Evolutionary Neural Network | |
CN106713190A (en) | MIMO (Multiple Input Multiple Output) transmitting antenna number blind estimation algorithm based on random matrix theory and feature threshold estimation | |
DE102019135900A1 (en) | Wireless communication device capable of quick beam selection and methods of operation thereof | |
CN116050504A (en) | Wind power short-term prediction model based on deep learning | |
Hu et al. | Two‐stage constant false alarm rate detection for distributed multiple‐input–multiple‐output radar | |
CN115473771A (en) | Model evolution-based environment sensing method | |
CN105425222B (en) | A kind of radar target detection method under data transmission rate constraint | |
CN113242066B (en) | Multi-cell large-scale MIMO communication intelligent power distribution method | |
Xue et al. | Deep learning based channel prediction for massive MIMO systems in high-speed railway scenarios | |
Bengio et al. | A connectionist system for medium-term horizon time series prediction | |
CN109686429A (en) | Physician visits period recommended method and device | |
CN106304108B (en) | MIMO radar mobile platform fast reserve dispositions method based on variation monitoring demand | |
CN105246173A (en) | Node clustering method suitable for RSN (Radar Sensor Network) and fusion decision method of node clustering method | |
CN103441806B (en) | Pure discontinuous Markov process spectrum sensing method for cognitive radio | |
CN109862625A (en) | A kind of shortwave radio monitor flexible networking method based on deep learning | |
CN111565431B (en) | Relay selection method and device of SWIPT cognitive radio network based on neural network | |
Bayo et al. | The percolating cluster is invisible to image recognition with deep learning | |
Li et al. | A lightweight mobile temporal convolution network for multi-location human activity recognition based on wi-fi | |
CN102820955B (en) | Secondary user's node is to the non-uniform quantizing method of frequency spectrum perception information |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |