CN108663667A - Range extension target detection method and system under the incomplete observation of radar - Google Patents
Range extension target detection method and system under the incomplete observation of radar Download PDFInfo
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- 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 embodiment of the present invention provides the range extension target detection method and system under a kind of incomplete observation of radar, and detection method includes：The reception data matrix of all range cells based on the radar, establishes binary hypothesis test model；By alternating iteration method, the first estimated value of the disturbance covariance matrix under the null hypothesis of binary hypothesis test model is obtained, and, the second estimated value of the disturbance covariance matrix under the alternative hvpothesis of binary hypothesis test model；The decision rule of detector based on the binary hypothesis test model obtains test statistics according to first estimated value and second estimated value and judges whether range extension target based on the test statistics.The present invention taken into account when parameter Estimation the low-rank characteristic of likelihood function maximization and clutter covariance matrix, and higher Parameter Estimation Precision can be obtained under incomplete observation scene.
Description
Technical field
The present embodiments relate to Radar Signal Processing Technology fields, more particularly, to a kind of incomplete observation of radar
Under range extension target detection method and system.
Background technology
The high-resolution characteristic of radar can obtain the more details information of detected object, make full use of these information effective
Improve the detection performance to target.With the raising of radar range resolution, the multiple Range resolutions of target scattering point range spans
Unit.This kind of size is more than the target of distance by radar resolution cell, commonly known as range extension target.
The existing range extension target detection method under Gaussian Clutter, white noise background is mainly from likelihood function
(Likelihood Function) sets out, using Generalized Likelihood Ratio (Generalized Likelihood Ratio Test,
GLRT), Rao is examined or Wald has inspected detection.But these methods are required to that a large amount of complete sights of unit to be checked can be obtained
Measured data, Part Methods also need to have identical statistical property with unit to be checked by enough and without the auxiliary of target echo
Data, to complete the estimation to detector parameters.However, since electromagnetic environment residing for radar is increasingly sophisticated, it tends to be difficult to obtain
The auxiliary data met the requirements.Also, in complex electromagnetic environment, radar is easily interfered by same category of device, other radiation sources, is caused
Observation data lack.For example, compression sampling can cause time domain data to lack, element failure causes airspace data to lack, frequency
Band interference causes frequency domain data to lack.It is this receive data when, sky, frequency some or certain several domains there is showing for shortage of data
As being referred to as incomplete observation.Under incomplete observation scene, due to shortage of data, current distance extension target detection method is equal
It will appear even significantly detection performance decline in various degree.
Invention content
In view of the problems of the existing technology, the embodiment of the present invention provides the extended distance under a kind of incomplete observation of radar
Object detection method and system.
The embodiment of the present invention provides a kind of range extension target detection method under the incomplete observation of radar, including：It is based on
The reception data matrix of all range cells of the radar, establishes binary hypothesis test model；By alternating iteration method, obtain
The first estimated value of the disturbance covariance matrix under the null hypothesis of the binary hypothesis test model is taken, and, the binary is false
If the second estimated value of the disturbance covariance matrix under the alternative hvpothesis of testing model；Based on the binary hypothesis test model
The decision rule of detector obtains test statistics according to first estimated value and second estimated value and is based on the inspection
It tests statistic and judges whether range extension target.
The embodiment of the present invention provides the range extension target detection system under a kind of incomplete observation of radar, including：Including：
Model building module is used for the reception data matrix of all range cells based on the radar, establishes binary hypothesis test mould
Type；Estimated value acquisition module, for by alternating iteration method, obtaining disturbing under the null hypothesis of the binary hypothesis test model
First estimated value of dynamic covariance matrix, and, the disturbance covariance square under the alternative hvpothesis of the binary hypothesis test model
Second estimated value of battle array；Detection module is used for the decision rule of the detector based on the binary hypothesis test model, according to institute
State the first estimated value and second estimated value obtain test statistics and based on the test statistics judge whether away from
From extension target.
The embodiment of the present invention provides the range extension target detection equipment under a kind of incomplete observation of radar, including：At least
One processor；And at least one processor being connect with the processor communication, wherein：The memory is stored with can quilt
The program instruction that the processor executes, the processor call described program instruction to be able to carry out above-mentioned detection method.
The embodiment of the present invention provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage
Medium storing computer instructs, and the computer instruction makes the computer execute above-mentioned detection method.
Range extension target detection method and system under the incomplete observation of radar provided in an embodiment of the present invention, by setting
It sets through alternating iteration method, obtains the first estimated value and the second estimated value so that detection method is not necessarily to auxiliary data, carries out
The low-rank characteristic of likelihood function maximization and clutter covariance matrix, the energy under incomplete observation scene have been taken into account when parameter Estimation
It accesses compared to the higher Parameter Estimation Precision of traditional detection method, and then ensure that better detection performance.The present invention is real
Other estimated values acquisition scene can be also individually used for by applying the detection method of example offer.
Description of the drawings
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 technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Some bright embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the flow chart of the range extension target detection embodiment of the method under the incomplete observation of radar of the present invention；
Fig. 2 is the detection performance figure for carrying out emulation experiment in the embodiment of the present invention under the incomplete observation of radar；
Fig. 3 is the module map of the range extension target detection system embodiment under the incomplete observation of radar of the present invention；
Fig. 4 is the frame signal of the range extension target detection equipment under the incomplete observation of radar in the embodiment of the present invention
Figure.
Specific implementation mode
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
The every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
Fig. 1 is the flow chart of the range extension target detection embodiment of the method under the incomplete observation of radar of the present invention, such as Fig. 1
It is shown, including：The reception data matrix of S101, all range cells based on the radar, establish binary hypothesis test model；
S102, by alternating iteration method, obtain of the disturbance covariance matrix under the null hypothesis of the binary hypothesis test model
One estimated value, and, the second estimated value of the disturbance covariance matrix under the alternative hvpothesis of the binary hypothesis test model；
The decision rule of S103, detector based on the binary hypothesis test model, according to first estimated value and described second
Estimated value obtains test statistics and judges whether range extension target based on the test statistics.
Specifically, step S101 refers to receiving data matrix according to the reception data acquisition of all range cells, and to institute
There is the reception data matrix of range cell to be grouped, obtains the reception data submatrix after several groupings, and based on described
Reception data submatrix after several groupings, establishes binary hypothesis test model.
Further, step S102 refers to, by alternating iteration method, obtaining the first estimated value and the second estimated value,
In, the first estimated value is the estimated value of the disturbance covariance matrix under the null hypothesis of binary hypothesis test model, the second estimated value
Be binary hypothesis test model alternative hvpothesis under disturbance covariance matrix estimated value.
Further, step S103 refers to that it is false that first estimated value and second estimated value are substituted into the binary
If in the decision rule of the detector of testing model, obtaining test statistics and being judged whether based on the test statistics
Range extension target.
Range extension target detection method under the incomplete observation of radar provided in an embodiment of the present invention is passed through by setting
Alternating iteration method obtains the first estimated value and the second estimated value so that detection method is not necessarily to auxiliary data, carries out parameter and estimates
Timing has taken into account the low-rank characteristic of likelihood function maximization and clutter covariance matrix, can be obtained under incomplete observation scene
Compared to the higher Parameter Estimation Precision of traditional detection method, and then it ensure that better detection performance.The embodiment of the present invention carries
The detection method of confession can also be individually used for other estimated values and obtain scene.
Based on above-described embodiment, the reception data matrix of all range cells based on the radar establishes binary
Hypothesis testing model specifically includes：The reception data of all range cells based on the radar, obtain all range cells
Receive data matrix；According to shortage of data location type, the reception data matrix procession of all range cells is set
Change the reception data submatrix after obtaining several groupings to be grouped to the reception data matrix；Based on described several
Reception data submatrix after a grouping, establishes binary hypothesis test model, wherein the binary hypothesis test model includes
Null hypothesis existing for no range extension target and there is alternative hvpothesis existing for range extension target.
It should be noted that several in the embodiment of the present invention refer to one or more.
Specifically, consider following scene：Radar has N number of receiving channel, and (receiving channel can represent array element, pulse or the two connection
Close, depending on concrete application scene), target to be checked spans up to K Range resolution unit.The data of original received data are lacked
Unsceptered set is filled with 0, and the kth row for being written as N × K dimensions matrix Z, wherein Z represent the reception data of k-th of range cell.To kth
A range cell receives data and is represented by the presence of having target
zk=nk+wk+αkP, k=1,2 ..., K,；
Wherein, zk、nk, p be N × 1 tie up column vector, zkTreated to fill out 0 receives data, nkIt is for known power
Receiver thermal noise, wkFor the noise signal received, αkFor target echo complex magnitude, p is known target guiding vector.
By nkAnd wkIt is collectively referred to as disturbance term dk, i.e. dk=nk+wk.Assuming that the disturbance term d of different distance unitkObey the zero-mean of i.i.d
Multiple Gauss is distributed, and is satisfied by same covariance matrix M=E { (zk-αkp)(zk-αkp)H, wherein () H representing matrixes are conjugated
Transposition.Then data matrix Z can be written as Z=D+p αT, wherein ()TRepresenting matrix transposition,
Since different distance cell data deletion sites may be identical, to reduce subsequent algorithm operation time, first basis
Matrix Z is divided into G group submatrixs by shortage of data location typeWherein Ng、KgIs indicated respectively
There are receiving channel, the range cell number of observation included in g grouping.The data rearrangement grouping process can be by data
The displacement of matrix Z processions is completed.
By the above process, the reception data submatrix after several groupings has been obtained.
Further, based on the reception data submatrix after several described groupings, binary hypothesis test model is established,
In, the binary hypothesis test model includes null hypothesis existing for no range extension target and has existing for range extension target
Alternative hvpothesis.
Wherein, binary hypothesis test model is as follows：
WhereinIndicate null hypothesis (no target presence),Indicate alternative hypothesis (with the presence of target)；MatrixThe matrix being made of 0,1 element determined for shortage of data position.
Based on above-described embodiment, the decision rule of the detector based on the binary hypothesis test model, according to institute
State the first estimated value and second estimated value obtain test statistics and based on the test statistics judge whether away from
From extension target, further include before：It obtains respectively under the null hypothesis and alternative hvpothesis in the binary hypothesis test model seemingly
It is false to obtain the binary based on the likelihood function under the null hypothesis and the likelihood function under the alternative hvpothesis for right function
If the decision rule of the detector of testing model.
It should be noted that the step of the present embodiment is between the detector based on the binary hypothesis test model
Decision rule, test statistics is obtained according to first estimated value and second estimated value and is based on the inspection statistics
Amount judges whether range extension target, and, the judgement of the detector based on the binary hypothesis test model is accurate
Then, test statistics is obtained according to first estimated value and second estimated value and is based on test statistics judgement
It is no that there are between range extension target.
Specifically, it obtainsWithLikelihood function under assuming thatWith
Expression formula be respectively：
With
Wherein,
After obtaining the likelihood function under the null hypothesis and alternative hvpothesis in the binary hypothesis test model respectively, it is based on institute
The likelihood function under null hypothesis and the likelihood function under the alternative hvpothesis are stated, the inspection of the binary hypothesis test model is obtained
The decision rule of device is surveyed, decision rule is as described in following formula：
Wherein,ForThe estimated value of lower M,Exist for { α, M }Under estimated value, η is decision threshold.
It is described by alternating iteration method based on above-described embodiment, obtain the null hypothesis of the binary hypothesis test model
Under disturbance covariance matrix the first estimated value, specifically include：By scheduled clutter covariance matrix iteration initial estimate
Target complex magnitude vector estimated value analytic expression is substituted into, obtains target complex magnitude vector iteration initial estimate, and initialize iteration
Number serial number；Newer clutter covariance matrix iterative estimate value is substituted into the target complex magnitude vector estimated value analytic expression,
Updated target complex magnitude vector estimated value is obtained, and updated first clutter covariance is obtained according to gradient project algorithms
Matrix Estimation value；Based on the updated target complex magnitude vector estimated value and the updated first clutter association side
Poor Matrix Estimation value, obtains the first relative changing value of updated object function, and judges that first relative changing value is
It is no to be more than the first default changing value thresholding；If first relative changing value is more than the first default changing value thresholding, by iteration
Number adds one, and newer first clutter covariance matrix iterative estimate value is substituted into the target complex magnitude vector estimated value solution
Analysis formula obtains updated target complex magnitude vector estimated value；If it is pre- that first relative changing value is less than or equal to first
If changing value thresholding, then according to the updated first clutter covariance matrix estimated value and receiver thermal noise power,
Obtain the first estimated value of the disturbance covariance matrix under null hypothesis.
Specifically, scheduled clutter covariance matrix iteration initial estimate is substituted into target complex magnitude vector estimated value solution
Analysis formula obtains target complex magnitude vector iteration initial estimate, and initializes iterations serial number, specifically includes：
By scheduled clutter covariance matrix iteration initial estimateSubstitute into the solution of target complex magnitude vector α estimated value
Analysis formula acquires target complex magnitude vector iteration initial estimate α(0), and iterations serial number l=1 is set.Target complex magnitude is sweared
The analytic expression for measuring α estimated values is as follows：
Wherein,
Further, by newer clutter covariance matrix iterative estimate valueSubstitute into the target complex magnitude vector α
Estimated value analytic expression obtains updated target complex magnitude vector estimated value α(l), and according to gradient project algorithms obtain update after
The first clutter covariance matrix estimated value
Further, it is based on the updated target complex magnitude vector estimated value and described updated first miscellaneous
Wave covariance matrix value, obtains the first relative changing value of updated object function, and judges the described first opposite change
Whether change value is more than the first default changing value thresholding.
Updated object function isIt is corresponded to
The first relative changing value be：
Further, if first relative changing value is more than the first default changing value thresholding, iterations are added one,
And newer first clutter covariance matrix iterative estimate value is substituted into the target complex magnitude vector estimated value analytic expression, it obtains
Updated target complex magnitude vector estimated value；If first relative changing value is less than or equal to the first default changing value door
Limit, then according to the updated first clutter covariance matrix estimated value and receiver thermal noise powerIt obtains former false
First estimated value of the disturbance covariance matrix set.First estimated value is
It is described to obtain updated first clutter covariance matrix estimation according to gradient project algorithms based on above-described embodiment
Value, specifically includes：According to reception data submatrix, default weight coefficient and the iteration step length after several described groupings, meter
The gradient projection matrix of the clutter covariance matrix is calculated, and updated second clutter is obtained based on the gradient projection matrix
Covariance matrix value；Based on the updated second clutter covariance matrix estimated value, updated target letter is obtained
The second several relative changing values, and judge whether second relative changing value is more than the second default changing value thresholding；If described
Second relative changing value is more than the second default changing value thresholding, then iterations is added one, and according to several described groupings after
Reception data submatrix, default weight coefficient and iteration step length, calculate the gradient projection square of the clutter covariance matrix
Battle array；If second relative changing value is less than or equal to the second default changing value thresholding, according to described updated second
Clutter covariance matrix estimated value and receiver thermal noise power obtain updated first clutter covariance matrix and estimate
Evaluation.
Specifically, iterations serial number i=1 is initialized first, according to reception data after several described groupings
Matrix, default weight coefficient and iteration step length, calculate the gradient projection matrix of the clutter covariance matrix, and based on described
Gradient projection matrix obtains updated second clutter covariance matrix estimated value.
Further, it is based on the updated second clutter covariance matrix estimated value, obtains updated target letter
The second several relative changing values, and judge whether second relative changing value is more than the second default changing value thresholding.
Updated second clutter covariance matrix estimated value is：
Wherein,(||·||*For nuclear norm, γ is power
Weight coefficient), MwGradientt(i-1)For iteration step
It is long,It indicates matrix negative feature value setting to 0 operation.
Second relative changing value is：
Further, if second relative changing value is more than the second default changing value thresholding, iterations are added one,
And according to reception data submatrix, default weight coefficient and the iteration step length after several described groupings, calculate the clutter
The gradient projection matrix of covariance matrix；If second relative changing value is less than or equal to the second default changing value thresholding,
Then according to the updated second clutter covariance matrix estimated value and receiver thermal noise powerDescribed in obtaining more
The first clutter covariance matrix estimated value after new
Based on above-described embodiment, second estimated value is obtained by following step：By scheduled clutter covariance matrix
Iteration initial estimate substitutes into target complex magnitude vector estimated value analytic expression, obtains target complex magnitude vector iteration initial estimation
Value, and initialize iterations serial number；Newer clutter covariance matrix iterative estimate value is substituted into the target complex magnitude arrow
Estimated value analytic expression is measured, obtains updated target complex magnitude vector estimated value, and after obtaining update according to gradient project algorithms
The first clutter covariance matrix estimated value；Based on the updated target complex magnitude vector estimated value and the update
The first clutter covariance matrix estimated value afterwards obtains the first relative changing value of updated object function, and described in judgement
Whether the first relative changing value is more than the first default changing value thresholding；If first relative changing value is more than the first default variation
It is worth thresholding, then iterations is added one, and newer first clutter covariance matrix iterative estimate value is substituted into the target and is answered
Amplitude vector estimated value analytic expression obtains updated target complex magnitude vector estimated value；If first relative changing value is small
In or be equal to the first default changing value thresholding, then according to the updated first clutter covariance matrix estimated value and connect
Receipts machine thermal noise power obtains the second estimated value of the disturbance covariance matrix under alternative hvpothesis.
It should be noted that the obtaining step of the second estimated value is identical the step of acquisition with the first estimated value, at this
In repeat no more.
Based on above-described embodiment, the decision rule of the detector based on the binary hypothesis test model, according to institute
State the first estimated value and second estimated value obtain test statistics and based on the test statistics judge whether away from
From extension target, specifically include：By first estimated value, second estimated value and the updated target complex magnitude
Vector estimated value substitutes into the decision rule of the detector of the binary hypothesis test model, obtains test statistics；Judge institute
State whether test statistics is more than preset decision threshold；If the test statistics is more than the preset decision threshold,
There are range extension targets；If the test statistics is less than or equal to the preset decision threshold, distance is not present
Extend target.
Fig. 2 is the detection performance figure for carrying out emulation experiment in the embodiment of the present invention under the incomplete observation of radar, please refers to figure
2, illustrate the detection performance figure using the object detection method.In this emulation, radar receiving channel N=20 is set,
Range cell number K=30 to be checked receives shortage of data position obedience and is uniformly distributed, miscellaneous noise ratio CNR=30dB, false-alarm probability PFA=
10-4, detection threshold passes through 100/PFASecondary Monte Carlo Experiment obtains.By figure it is observed that as shortage of data rate τ drops
Low or rank of matrix r reduces, and curve all integrally moves to left.Illustrate that the reduction of shortage of data rate, rank of matrix reduction can all to be proposed
The detection performance of method is improved.For example, working as τ=0.2, when r is reduced to 3 by 7, reach 80% detection probability to believing miscellaneous noise ratio
Requirement reduce 2.9dB.
Based on above-described embodiment, Fig. 3 is that the range extension target detection system under the incomplete observation of radar of the present invention is implemented
The module map of example, including：Model building module 301 is used for the reception data matrix of all range cells based on the radar,
Establish binary hypothesis test model；Estimated value acquisition module 302, for by alternating iteration method, obtaining the dualism hypothesis
First estimated value of the disturbance covariance matrix under the null hypothesis of testing model, and, the binary hypothesis test model it is standby
Second estimated value of the disturbance covariance matrix under choosing hypothesis；Detection module 303, for being based on the binary hypothesis test model
Detector decision rule, test statistics is obtained according to first estimated value and second estimated value and based on described
Test statistics judges whether range extension target.
The detecting system of the embodiment of the present invention can be used for executing the extended distance under the incomplete observation of radar shown in FIG. 1
The technical solution of object detection method embodiment, implementing principle and technical effect are similar, and details are not described herein again.
Based on above-described embodiment, Fig. 4 is the range extension target inspection under the incomplete observation of radar in the embodiment of the present invention
The block schematic illustration of measurement equipment.Referring to FIG. 4, the embodiment of the present invention provides the extended distance mesh under a kind of incomplete observation of radar
Detection device is marked, including：Processor (processor) 410, is deposited at communication interface (Communications Interface) 420
Reservoir (memory) 430 and bus 440, wherein processor 410, communication interface 420, memory 430 are completed by bus 440
Mutual communication.Processor 410 can call the logical order in memory 430, to execute following method, including：It is based on
The reception data matrix of all range cells of the radar, establishes binary hypothesis test model；By alternating iteration method, obtain
The first estimated value of the disturbance covariance matrix under the null hypothesis of the binary hypothesis test model is taken, and, the binary is false
If the second estimated value of the disturbance covariance matrix under the alternative hvpothesis of testing model；Based on the binary hypothesis test model
The decision rule of detector obtains test statistics according to first estimated value and second estimated value and is based on the inspection
It tests statistic and judges whether range extension target.
The embodiment of the present invention discloses a kind of computer program product, and the computer program product is non-transient including being stored in
Computer program on computer readable storage medium, the computer program include program instruction, when described program instructs quilt
When computer executes, computer is able to carry out the expansion method that above-mentioned each method embodiment is provided, such as including：Based on described
The reception data matrix of all range cells of radar, establishes binary hypothesis test model；By alternating iteration method, institute is obtained
The first estimated value of the disturbance covariance matrix under the null hypothesis of binary hypothesis test model is stated, and, the dualism hypothesis inspection
Test the second estimated value of the disturbance covariance matrix under the alternative hvpothesis of model；Detection based on the binary hypothesis test model
The decision rule of device is obtained test statistics according to first estimated value and second estimated value and is united based on the inspection
Metering judges whether range extension target.
Based on above-described embodiment, the embodiment of the present invention provides a kind of non-transient computer readable storage medium, described non-temporary
State computer-readable recording medium storage computer instruction, it is real that the computer instruction makes the computer execute above-mentioned each method
Apply the expansion method that example is provided, such as including：The reception data matrix of all range cells based on the radar, establishes two
First hypothesis testing model；By alternating iteration method, the disturbance association side under the null hypothesis of the binary hypothesis test model is obtained
First estimated value of poor matrix, and, of disturbance covariance matrix under the alternative hvpothesis of the binary hypothesis test model
Two estimated values；The decision rule of detector based on the binary hypothesis test model, according to first estimated value and described
Second estimated value obtains test statistics and judges whether range extension target based on the test statistics.
One of ordinary skill in the art will appreciate that：Realize that above equipment embodiment or embodiment of the method are only schematic
, wherein can be that physically separate component may not be physically separated for the processor and the memory, i.e.,
A place can be located at, or may be distributed over multiple network units.It can select according to the actual needs therein
Some or all of module achieves the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creative labor
In the case of dynamic, you can to understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It is realized by the mode of software plus required general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be expressed in the form of software products in other words, should
Computer software product can store in a computer-readable storage medium, such as USB flash disk, mobile hard disk, ROM/RAM, magnetic disc, CD
Deng, including some instructions use is so that a computer equipment (can be personal computer, server or the network equipment etc.)
Execute the method described in certain parts of each embodiment or embodiment.
Finally it should be noted that：The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, it will be understood by those of ordinary skill in the art that：It still may be used
With technical scheme described in the above embodiments is modified or equivalent replacement of some of the technical features；
And these modifications or replacements, various embodiments of the present invention technical solution that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of range extension target detection method under incomplete observation of radar, which is characterized in that including：
The reception data matrix of all range cells based on the radar, establishes binary hypothesis test model；
By alternating iteration method, first of the disturbance covariance matrix under the null hypothesis of the binary hypothesis test model is obtained
Estimated value, and, the second estimated value of the disturbance covariance matrix under the alternative hvpothesis of the binary hypothesis test model；
The decision rule of detector based on the binary hypothesis test model is estimated according to first estimated value and described second
Evaluation obtains test statistics and judges whether range extension target based on the test statistics.
2. detection method according to claim 1, which is characterized in that all range cells based on the radar
Data matrix is received, binary hypothesis test model is established, specifically includes：
The reception data of all range cells based on the radar obtain the reception data matrix of all range cells；
According to shortage of data location type, the reception data matrix procession of all range cells is replaced with to described
It receives data matrix to be grouped, obtains the reception data submatrix after several groupings；
Based on the reception data submatrix after several described groupings, binary hypothesis test model is established, wherein the binary is false
If testing model includes null hypothesis existing for no range extension target and has alternative hvpothesis existing for range extension target.
3. detection method according to claim 2, which is characterized in that the inspection based on the binary hypothesis test model
The decision rule for surveying device obtains test statistics according to first estimated value and second estimated value and is based on the inspection
Statistic judges whether range extension target, further includes before：
The likelihood function under the null hypothesis and alternative hvpothesis in the binary hypothesis test model is obtained respectively, based on described former false
Likelihood function under the likelihood function and the alternative hvpothesis set, obtains the detector of the binary hypothesis test model
Decision rule.
4. detection method according to claim 1, which is characterized in that it is described by alternating iteration method, obtain described two
First estimated value of the disturbance covariance matrix under the null hypothesis of first hypothesis testing model, specifically includes：
Scheduled clutter covariance matrix iteration initial estimate is substituted into target complex magnitude vector estimated value analytic expression, obtains mesh
Complex magnitude vector iteration initial estimate is marked, and initializes iterations serial number；
Newer clutter covariance matrix iterative estimate value is substituted into the target complex magnitude vector estimated value analytic expression, is obtained more
Target complex magnitude vector estimated value after new, and obtain updated first clutter covariance matrix according to gradient project algorithms and estimate
Evaluation；
Estimated based on the updated target complex magnitude vector estimated value and updated first clutter covariance matrix
Evaluation, obtains the first relative changing value of updated object function, and judges whether first relative changing value is more than the
One default changing value thresholding；
If first relative changing value is more than the first default changing value thresholding, iterations are added one, and by newer the
One clutter covariance matrix iterative estimate value substitutes into the target complex magnitude vector estimated value analytic expression, obtains updated target
Complex magnitude vector estimated value；If first relative changing value is less than or equal to the first default changing value thresholding, according to institute
Updated first clutter covariance matrix estimated value and receiver thermal noise power are stated, the disturbance association side under null hypothesis is obtained
First estimated value of poor matrix.
5. detection method according to claim 4, which is characterized in that described updated according to gradient project algorithms acquisition
First clutter covariance matrix estimated value, specifically includes：
According to reception data submatrix, default weight coefficient and the iteration step length after several described groupings, calculate described miscellaneous
The gradient projection matrix of wave covariance matrix, and updated second clutter covariance square is obtained based on the gradient projection matrix
Battle array estimated value；
Based on the updated second clutter covariance matrix estimated value, the second opposite change of updated object function is obtained
Change value, and judge whether second relative changing value is more than the second default changing value thresholding；
If second relative changing value is more than the second default changing value thresholding, iterations are added one, and if according to described
Reception data submatrix, default weight coefficient after dry grouping and iteration step length, calculate the clutter covariance matrix
Gradient projection matrix；If second relative changing value is less than or equal to the second default changing value thresholding, according to more
The second clutter covariance matrix estimated value after new and receiver thermal noise power obtain the updated first clutter association
Variance matrix estimated value.
6. detection method according to claim 1, which is characterized in that second estimated value is obtained by following step：
Scheduled clutter covariance matrix iteration initial estimate is substituted into target complex magnitude vector estimated value analytic expression, obtains mesh
Complex magnitude vector iteration initial estimate is marked, and initializes iterations serial number；
Newer clutter covariance matrix iterative estimate value is substituted into the target complex magnitude vector estimated value analytic expression, is obtained more
Target complex magnitude vector estimated value after new, and obtain updated first clutter covariance matrix according to gradient project algorithms and estimate
Evaluation；
Estimated based on the updated target complex magnitude vector estimated value and updated first clutter covariance matrix
Evaluation, obtains the first relative changing value of updated object function, and judges whether first relative changing value is more than the
One default changing value thresholding；
If first relative changing value is more than the first default changing value thresholding, iterations are added one, and by newer the
One clutter covariance matrix iterative estimate value substitutes into the target complex magnitude vector estimated value analytic expression, obtains updated target
Complex magnitude vector estimated value；If first relative changing value is less than or equal to the first default changing value thresholding, according to institute
Updated first clutter covariance matrix estimated value and receiver thermal noise power are stated, the disturbance association under alternative hvpothesis is obtained
Second estimated value of variance matrix.
7. detection method according to claim 4, which is characterized in that the inspection based on the binary hypothesis test model
The decision rule for surveying device obtains test statistics according to first estimated value and second estimated value and is based on the inspection
Statistic judges whether range extension target, specifically includes：
First estimated value, second estimated value and the updated target complex magnitude vector estimated value are substituted into institute
In the decision rule for stating the detector of binary hypothesis test model, test statistics is obtained；
Judge whether the test statistics is more than preset decision threshold；
If the test statistics is more than the preset decision threshold, there are range extension targets；If the inspection statistics
Amount is less than or equal to the preset decision threshold, then range extension target is not present.
8. the range extension target detection system under a kind of incomplete observation of radar, which is characterized in that including：
Model building module is used for the reception data matrix of all range cells based on the radar, establishes dualism hypothesis inspection
Test model；
Estimated value acquisition module is used for through alternating iteration method, under the null hypothesis for obtaining the binary hypothesis test model
First estimated value of disturbance covariance matrix, and, the disturbance covariance under the alternative hvpothesis of the binary hypothesis test model
Second estimated value of matrix；
Detection module is used for the decision rule of the detector based on the binary hypothesis test model, estimates according to described first
Value and second estimated value obtain test statistics and judge whether range extension target based on the test statistics.
9. the range extension target detection equipment under a kind of incomplete observation of radar, which is characterized in that including：
At least one processor；And
At least one processor being connect with the processor communication, wherein：
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
Enough detection methods executed as described in claim 1 to 7 is any.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute the detection method as described in claim 1 to 7 is any.
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