CN105137420B - A kind of incoherent MIMO radar detection of Frame accumulation and localization method - Google Patents
A kind of incoherent MIMO radar detection of Frame accumulation and localization method Download PDFInfo
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- CN105137420B CN105137420B CN201510569900.2A CN201510569900A CN105137420B CN 105137420 B CN105137420 B CN 105137420B CN 201510569900 A CN201510569900 A CN 201510569900A CN 105137420 B CN105137420 B CN 105137420B
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/66—Radar-tracking systems; Analogous systems
- G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
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Abstract
A kind of incoherent MIMO radar detection of Frame accumulation of the disclosure of the invention and localization method, belong to Radar Signal Processing Technology field.This method is directed to the weak target under low signal-to-noise ratio environment, single frames does not do threshold processing, value function is used as using joint log-likelihood ratio function, the value function of continuous several frames is subjected to Frame accumulation according to the state transfer relationship of target, the information of target multiframe is made full use of, the estimated location of testing result and target is announced after handling multiframe data aggregate simultaneously.It efficiently solves the problem of big position error that the weak target under low signal-to-noise ratio environment is likely to occur and missing inspection, and more existing maximum Likelihood can obtain more preferable detection probability and Geng Gao positioning precision.
Description
Technical field
The invention belongs to Radar Signal Processing Technology field, more particularly to incoherent MIMO radar location technology and radar are micro-
Faint target detection technical field.
Background technology
In recent years, MIMO radar is of increased attention as a kind of New Type Radar system.Incoherent MIMO thunders
Separate in space farther out up to due to its antenna, space diversity gain can be obtained.Therefore, the high accuracy of incoherent MIMO radar
Orientation problem causes the great interest of lot of domestic and foreign scholar.However, due to the presence of various uncertain factors, it is incoherent
The high accuracy positioning of MIMO radar becomes more rich in challenge.The existing signal level localization method based on echo-signal, leads to
Target positioning is carried out frequently with maximal possibility estimation.But this method is just with the metrical information of target single frames, for low
Weak target under the conditions of signal to noise ratio, it may appear that position error is big, missing inspection the problems such as.For the moving target in space, according to
The motion model of target, the position of actually target consecutive frame is height correlation.
Dynamic programming algorithm is a kind of technology for carrying out detecting and tracking to weak target under low signal-to-noise ratio environment.It is in list
Frame does not do Threshold detection, but frame data is quantified and stored, after multiframe echo data Combined Treatment, announces simultaneously
The estimated state of testing result and target.It is easily achieved and can handle weak mobility target, has been widely used in radar
Target detection tracks field.Document " Noncoherent MIMOradar for location and velocity
estimation:More antennas means betterperformance " are using the signal level based on maximal possibility estimation
The position of localization method estimation target and velocity information, but this method only utilizes the spatial information of target present frame, has given up mesh
Information included in mark a few frame position estimations in the past；Document " Target localization and tracking in
Noncoherent multiple-input multiple-output radarsystems " are utilized respectively Kalman filtering calculation
Method and particle filter algorithm realize positioning and tracking of the incoherent MIMO radar to target.But when target is in low signal-to-noise ratio
When in environment, the echo-signal of this method can be flooded by noise, will appear from weak target missing inspection or positioning precision drastically
The problems such as decline.
The content of the invention
The present invention is to provide a kind of the non-of Frame accumulation for the weak point technical problem to be solved of background technology
Relevant MIMO radar detection and localization method, reach the mesh that target detection and positioning precision are improved in the environment of low signal-to-noise ratio
's.
The present invention is a kind of incoherent MIMO thunders that the technical scheme that is used of solution above-mentioned technical problem is Frame accumulation
Up to detection and localization method, comprise the following steps：
Step 1, initialization systematic parameter include：The corresponding datum plane size in target proximity hunting zone, radar emission
The quantity of machine and receiver, the position of each transmitter receiver, observation interval T, Dynamic Programming processing totalframes K, thresholding VT；
Step 2, K frame echo-signals are received, datum plane is subjected to mesh generation, calculate each passage in each grid
Log-likelihood ratio function, obtains joint likelihood ratio by the log-likelihood ratio summation of each passage of each grid, joint likelihood is compared to
For the value function of the grid；
Step 3, according to state shift accumulation dynamic planning value function：
If k=1 frames, value function corresponding to each grid of the frame assigns initial value, and the value function initial value of each grid of the frame is each
The joint log-likelihood ratio of the frame of grid first；
If 2≤k≤K frames, the corresponding value function of grid (i, j) is updated；It is assumed that there is one in kth frame grid (i, j)
Target, determines that the frame of kth -1 is possible to be transferred to θ according to state transfer relationshipkRegion, θkFor the state of kth frame target, seek
Look for the maximum grid of the region median function and record the coordinate of the grid, i.e. state θkCorresponding previous frame state, then will
The value function maximum found out in k-1 frames is overlapped with the LLR ratio of combining of grid where kth frame same target, will
Grid where value after superposition is assigned to kth frame same target, the value function of each grid of kth frame is updated with this；
If step 4, k<K, makes k=k+1, return to step 2, and k is frame number, and K is total measurement frame number；
Step 5, k=K frames, threshold processing：
VTThresholding is represented, for k-th frame, if the maximum of target value function is higher than thresholding, then it is assumed that the grid has mesh
Mark is present；If the maximum of target value function is below thresholding, declaration is not detected by target, and the thresholding is set according to actual conditions
It is fixed；
The target position information for the target k-th frame that step 6, the target exploitation step 3 for assert step 5 are recorded is as right
Answer the estimated location of target.
The present invention is directed to the weak target under low signal-to-noise ratio environment, and feature is the motion model using target, and single frames is not done
Threshold processing, using joint log-likelihood ratio function as value function, the value function of continuous several frames is turned according to the state of target
Shifting relation carries out Frame accumulation, makes full use of the information of target multiframe, announces detection knot after handling multiframe data aggregate simultaneously
Fruit and the estimated location of target.It efficiently solve position error that the weak target under low signal-to-noise ratio environment is likely to occur it is big and
The problem of missing inspection, more existing maximum Likelihood can obtain more preferable detection probability and Geng Gao positioning precision.
The beneficial effects of the invention are as follows can realize to the joint-detection of weak target and fixed under low signal-to-noise ratio environment
Position, obtains preferably detection performance and Geng Gao positioning precision.
Brief description of the drawings
Fig. 1 is FB(flow block) of the invention.
Fig. 2 accumulates the comparison diagram of the detection probability of different frame numbers and the detection probability of maximal possibility estimation for the present invention.
Fig. 3 accumulates the root-mean-square error and existing maximal possibility estimation of the estimation target location of different frame numbers for the present invention
Root-mean-square error comparison diagram.
Embodiment
The main method for using Computer Simulation of the invention is verified that all steps, conclusion are all in MATLAB-R2010b
Upper checking is correct.Specific implementation step is as follows：
Step 1, initialization systematic parameter：
Initialization systematic parameter includes：The corresponding datum plane size in target proximity hunting zone, radar transmitter number
M, receiver number N, the position of each transmitter and receiver, the transmission signal waveform s of each radar transmitterh(t), h=1,
2 ... M, Dynamic Programming processing totalframes K, observation interval T, state transfer number q, decision threshold VT。
Step 2, obtain K frame value function planes：
2.1. for each mesh point (x, y), the echo time delay τ of each passage is calculatedlk：
Wherein, c is the light velocity,Respectively each cell site of radar, the coordinate of receiving station.
2.2. the measuring value z of the echo data of kth frame is read from radar receiverk, Represent measurement of the lh passages of kth frame echo data in mesh point (i, j)
Value, NxThe unit sum quantified for x-axis, NyThe unit sum quantified for y-axis.When having target：
When there is no target：
2.3. by measured value discretization in time, obtainNsFor one
The sampling number of frame in.
2.4. the corresponding joint LLR ratio of each mesh point is calculated：
WhereinWhen indicating targetLikelihood function,Expression does not have
When having targetLikelihood function.
C is a constant independently of dbjective state.
2.5. repeat step 2.1 to 2.4, until all mesh points in traversal K frame data planes, obtains K frames total according to flat
The log-likelihood ratio function image in face.
Step 3, according to state shift accumulation dynamic planning value function：
If k=1 frames, value function corresponding to the moment dbjective state assigns initial value, the value function of the moment dbjective state
Initial value is the corresponding first frame log-likelihood ratio plane of dbjective state；
If 2≤k≤K frames, the corresponding value function of each mesh point is updated；Represent kth frame
Any quantization state,Expression stateCorresponding value function,Represent that the frame of kth -1 is possible to be transferred to
θkState set,Region that may be present is (xk+δ1-vx,k,yk+δ2-vy,k), wherein δ1,δ2Possibility span
ForOperatorExpression is rounded downwards, and the more new relation of value function is
If step 4, k<K, makes k=k+1, return to step 3, and k is frame number, and K is total processing frame number.
Step 5, k=K frames, threshold processing：
VTThresholding is represented, for k-th frame, if the maximum of target value function is higher than thresholding, then it is assumed that the mesh point has
Target is present；If the value function of target is below thresholding, declaration is not detected by target.
The target position information for the target k-th frame that step 6, the target exploitation step 3 for assert step 5 are recorded is as right
Answer the estimated location of target.
Claims (1)
1. a kind of incoherent MIMO radar detection of Frame accumulation and localization method, comprise the following steps：
Step 1, initialization systematic parameter include：The corresponding datum plane size in target proximity hunting zone, radar transmitter and
The quantity of receiver, the position of each transmitter receiver, observation interval T, Dynamic Programming processing totalframes K, thresholding VT；
Step 2, K frame echo-signals are received, datum plane is subjected to mesh generation, calculate the logarithm of each passage in each grid
Likelihood ratio function, obtains joint log-likelihood ratio by the log-likelihood ratio summation of each passage of each grid, will combine log-likelihood
It is used for the value function of the grid；
Step 3, according to state shift accumulation dynamic planning value function：
If k=1 frames, value function corresponding to each grid of the frame assigns initial value, and the value function initial value of each grid of the frame is each grid
The joint log-likelihood ratio of first frame；
If 2≤k≤K frames, the corresponding value function of grid (i, j) is updated；It is assumed that there is a target in kth frame grid (i, j),
Determine that the frame of kth -1 is possible to be transferred to θ according to state transfer relationshipkRegion, θkFor the state of kth frame target, the area is found
The maximum grid of domain median function and the state for recording the grid, i.e. state θkCorresponding previous frame state, then by k-1 frames
The value function maximum found out is overlapped with the LLR ratio of combining of grid where kth frame same target, after superposition
Value be assigned to grid where kth frame same target, the value function of each grid of kth frame is updated with this；
If step 4, k<K, makes k=k+1, return to step 3, and k is frame number, and K is total measurement frame number；
Step 5, k=K frames, threshold processing：
VTThresholding is represented, for k-th frame, if the maximum of target value function is higher than thresholding, then it is assumed that the grid has target to deposit
；If the maximum of target value function is below thresholding, declaration is not detected by target, and the thresholding is set according to actual conditions；
The target position information for the target k-th frame that step 6, the target exploitation step 3 for assert step 5 are recorded is used as correspondence mesh
Target estimated location.
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Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105807267B (en) * | 2016-03-11 | 2018-01-09 | 中国人民解放军国防科学技术大学 | A kind of MIMO radar extends mesh object detection method |
| CN105978832B (en) * | 2016-04-28 | 2019-02-19 | 西安电子科技大学 | A kind of channel compensation and signal detecting method based on interchannel noise dynamic estimation |
| CN106033120B (en) * | 2016-06-29 | 2018-04-06 | 电子科技大学 | A kind of asynchronous multi-frame joint detection method of multistation radar |
| CN107340517B (en) * | 2017-07-04 | 2021-02-05 | 电子科技大学 | Multi-sensor multi-frame tracking-before-detection method |
| CN108333571B (en) * | 2018-02-07 | 2020-04-21 | 电子科技大学 | Multi-sensor multi-frame joint detection tracking method based on trace point sequence fusion |
| CN111295596A (en) * | 2019-02-28 | 2020-06-16 | 深圳市大疆创新科技有限公司 | Method and device for measuring angle of millimeter wave radar and storage medium |
| CN110231616B (en) * | 2019-04-09 | 2021-01-12 | 电子科技大学 | Sea surface moving target detection and positioning method based on Beidou satellite radiation source |
| CN112703421B (en) * | 2019-05-24 | 2021-12-03 | 华为技术有限公司 | Echo signal processing method, device, system and storage medium |
| CN111224724A (en) * | 2019-11-06 | 2020-06-02 | 重庆邮电大学 | Path loss measuring method based on intelligent medical service |
| CN113341391B (en) * | 2021-06-01 | 2022-05-10 | 电子科技大学 | Radar target multi-frame joint detection method in unknown environment based on deep learning |
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