CN110109094A - The detection of multi-receiver station single frequency network external illuminators-based radar maneuvering target and tracking - Google Patents
The detection of multi-receiver station single frequency network external illuminators-based radar maneuvering target and tracking Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
- G01S13/88—Radar or analogous systems specially adapted for specific applications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
Abstract
A kind of detection of multi-receiver station single frequency network external illuminators-based radar maneuvering target disclosed by the invention and tracking can efficiently realize maneu-vering target detection and tracking using the present invention.The technical scheme is that: it is tracked under frame before the detection based on particle filter, measurement model module receives the initial data after relevant treatment, according to the measurement initial data of corresponding scene and acquisition, generalized fuzzy function is provided, establishes the metric data model of receiving station；Likelihood ratio function module provides single goal likelihood ratio function according to metric data model and received measurement initial data, for Swerling I and Swerling III target fluctuation model, calculates target likelihood ratio function using the method for Monte Carlo integral；Algorithm implements module, and according to target, there are transfer matrix predicts that sampling instant target there are variable, according to the generation probability of initial model, calculates the probability of each motion model, realizes detection and tracking.
Description
Technical field
The present invention relates to digital audio/video signal, digital broadcasting-television signal, digital multimedia broadcasting signal, number electricity
Depending on the object detecting and tracking field of the single frequency network external radiation source radar system of ground multimedia broadcast singal, and in particular to connect more
Station single frequency network external radiation source radar system is received to tracking (TBD) method before a kind of detection of maneuvering target.
Technical background
External illuminators-based radar is also known as passive radar, which refers to the miscoordination radiation source electricity for passively receiving target reflection
Magnetic signal carries out the radar system of detection and tracking to target.Passive radar only has receiving device, therefore system is relatively easy, easily
It is cheap in deployment, production and maintenance cost.Passive radar can be divided into according to the difference of detected object and utilize detected target
Itself radiation carries out target detection, positioning and tracking, and carries out target acquisition using the electromagnetic wave of miscoordination irradiation source transmitting
With tracking two major classes.External illuminators-based radar is dedicated to getting rid of the constraint of emission source, can utilize a large amount of existing irradiation sources
Realize that (current main miscoordination side includes broadcasting station, TV station, communication station, direct broadcast system for target acquisition
(DBS), global positioning system (GPS), monostatic radar on various platforms etc.).External illuminators-based radar at work, passes through antenna
Reception forms anti-after direct wave and foreign radiation sources irradiation target from external miscoordination radiation source (third party)
Ejected wave or scattered wave receive the information such as the time difference of signal, Doppler frequency difference and angle of arrival using the multistation that it is carried, through locating
Target information is extracted after reason and eliminates garbage and interference, to complete the detection, positioning and tracking to target.
When external illuminators-based radar carries out target acquisition, usual target echo energy is all smaller.It just needs at this time logical
Temporal accumulation is crossed to improve the signal-to-noise ratio of detection, while receiving station can not eliminate the through wavelength-division in echo-signal completely
Amount, it is therefore desirable to the signal-to-noise ratio and signal to noise ratio of detection are improved by temporal accumulation.Since receiving station is not ideal line
System, receiving channel and reference channel it is inconsistent and system it is non-linear limit the performance of clutter recognition, it is in general miscellaneous
Wave inhibits ratio between 20-40dB.In most cases, waveform is not ideal drawing pin type, is unfavorable for the detection of target.
It is serious to bring if unavoidably bringing the secondary lobe on distance or Doppler using matched filtering, influencing detection performance
Range ambiguity.
In recent years, the research and application of external illuminators-based radar just develop system to towards to more transmitting-receivings from singly receiving and dispatching.When
Single frequency network external radiation source radar system is using the external illuminators-based radar of same frequency digital quadrature frequency division multiplex OFDM signal to target
When carrying out detection and tracking, it will face a critically important problem: can be generated simultaneously to same target in each receiving station more
A measuring value, it is to be generated by which transmitting station by the echo-signal that target reflects that these measuring values, which not can determine that, this problem
It is referred to as observed quantity source uncertain problem in Research Literature.However single frequency network external radiation source radar system is in grinding before
In studying carefully, the step of data correlation is increased to measurement information is required to be associated with the relationship between measurement and transmitting station.But with
The increase of transmitting station's quantity and the reduction of signal-to-noise ratio, it can bring complicated and heavy calculation amount, lead to the appearance of np hard problem,
(such as 1 target is generated 7 targets by the irradiation of 10 transmitting stations to the performance and real-time for having seriously affected target detection and tracking
It measures (there are 3 missing inspections) additional 5 false-alarms to measure, then relevance assumption number maximum is up to 2581284541), when hard decision is calculated
When method associated errors, then it will appear target " ghost " problem, and due to the variation of target flight posture, it will target is caused to be returned
Wave amplitude rises and falls, and this fluctuations have seriously affected the detection of radar.Therefore it is badly in need of one kind and realizes single-frequency under low signal-to-noise ratio
The high efficiency method of net external illuminators-based radar object detecting and tracking.
Summary of the invention
Generated " ghost when it is an object of the invention to use hard decision algorithm associated errors before eliminating in academic research
Shadow " problem, and alleviate existing method the problem of low signal-to-noise ratio performance declines, propose that a kind of TBD based on particle filter is calculated
Method, can efficiently realize maneu-vering target detection and the tracking of single frequency network external radiation source radar system, and can apply in engineering.
In order to achieve the above-mentioned object of the invention, the present invention provides a kind of multi-receiver station single frequency network external illuminators-based radars to motor-driven
The detection of target and tracking, it is characterised in that include the following steps: under target echo amplitude scintillation environment, each receiving station
Relevant treatment is carried out using the reception signal and reference signal of kth sampling instant, using normed space apodization (SVA) technology to phase
The result for closing processing is handled, and is obtained and is measured initial data；It is tracked under frame before the detection based on particle filter, measures mould
Initial data after pattern block reception relevant treatment is provided according to the measurement initial data of corresponding scene and acquisition by SVA skill
Generalized fuzzy function after art processing, establishes the metric data model of receiving station, is applied to subsequent likelihood ratio function module
Implement module with algorithm；Likelihood ratio function module provides i-th and connects according to metric data model and received measurement initial data
The single goal likelihood ratio function for receiving the cell (m, n) at station, for Swerling I and Swerling III target fluctuation model,
Target likelihood ratio function is calculated using the method for Monte Carlo integral, according to the mutual independence and MIMO measured between receiving station
The likelihood ratio function representation form of radar derives single goal likelihood ratio function；Algorithm implements module and there is transfer square according to target
There are variables to predict kth sampling instant target by battle array ΘThe measurement region more than a certain detection threshold is chosen, in the measurement area
The uniform sampling site in domain counts the quantitative variation of its corresponding target movement model type, according to first as newborn intended particle respectively
The generation probability of beginning model, calculates the probability of each motion model, with tracking (MM-PF- before the particle filter detection of multi-model
TBD) algorithm realizes detection and tracking of the maneuvering target under low signal-to-noise ratio.
The present invention has the following beneficial effects: compared with the prior art
The present invention is mutually indepedent using the measurement information between receiving station, derives multi-receiver station under target fluctuation model
Single frequency network external radiation source radar system single goal likelihood ratio function gives aobvious based on the approximate likelihood ratio function in Monte Carlo
Formula solution overcomes caused by wherein integral term without Problem of Analytical.
The present invention directly avoids the data correlation process of traditional tracking, using sentencing firmly in academic research before elimination
Generated " ghost " problem when annual reporting law associated errors avoids data correlation step and measures between transmitting station in association
The np hard problem occurred when relationship improves the real-time of multi-receiver station single frequency network external radiation source radar system object detecting and tracking
Property.
The present invention makes full use of detect before track algorithm advantage, realize detection of the maneuvering target under low signal-to-noise ratio with
Track effectively alleviates existing method the problem of low signal-to-noise ratio performance declines, improves maneuvering target under low signal-to-noise ratio
Detection and tracking performance.
Detailed description of the invention
Fig. 1 is multi-receiver station single frequency network external illuminators-based radar schematic diagram of a scenario applied by the present invention.
Fig. 2 is object detecting and tracking flow chart of the multi-receiver station single frequency network external illuminators-based radar to maneuvering target.
Fig. 3 is the maneuvering target existing probability of the invention comparison diagram under different signal-to-noise ratio.
Fig. 4 is the maneuvering target position RMSE of the invention comparison diagram under different signal-to-noise ratio.
Fig. 5 is maneuvering target motion model probability Estimation figure of the invention.
Fig. 6 is the maneuvering target existing probability of the invention comparison diagram under different receiving station's quantity.
Fig. 7 is the maneuvering target position RMSE of the invention comparison diagram under different receiving station's quantity.
Specific embodiment
Refering to fig. 1, Fig. 2.In multi-receiver station single frequency network external illuminators-based radar scene, contain NtA transmitting station SFN (packet
It includes: the transmitting station SFN 1, the transmitting station SFN 2 ..., the transmitting station SFN Nt) and Nr(receiving channel is contained in each receiving station for a receiving station
And reference channel, including: receiving channel 1, receiving channel 2 ..., receiving channel NrWith reference channel 1, reference channel
2 ..., reference channel Nr), and monitor and contain a target in region.After reference signal purification first restores in each receiving station,
Then relevant treatment is carried out with the reception signal after direct path cancellation and clutter recognition, the data after relevant treatment is not done any
Thresholding operation is crossed, finally unifies to be sent into data fusion center, successively using measurement model module, likelihood ratio function module and algorithm
Implement module, (i.e. target trajectory is to meet certain rule and make an uproar to the otherness using target and noise at each moment
Sound is then unordered), realize detection and tracking of the maneuvering target under low signal-to-noise ratio.
According to the present invention, under target echo amplitude scintillation environment, each receiving station utilizes the reception signal of sampling instant
Relevant treatment is carried out with reference signal, the result of relevant treatment is handled using normed space apodization (SVA) technology, is obtained
Measure initial data；It is tracked under frame before the detection based on particle filter, measurement model module receives the original after relevant treatment
Beginning data provide the generalized fuzzy letter after SVA technical treatment according to the measurement initial data of corresponding scene and acquisition
Number, establishes the metric data model of receiving station, is applied to subsequent likelihood ratio function module and algorithm implements module；Likelihood ratio letter
Digital-to-analogue root tuber is according to metric data model and the received monocular for measuring initial data and providing the cell (m, n) of i-th of receiving station
Likelihood ratio function is marked, for Swerling I and Swerling III target fluctuation model, the method integrated using Monte Carlo
Target likelihood ratio function is calculated, the likelihood ratio function representation shape according to the mutual independence measured and MIMO radar between receiving station
Formula derives single goal likelihood ratio function；Algorithm implements module, and according to target, there are transfer matrix Θ predicts kth sampling instant mesh
There are variables for markThe measurement region more than a certain detection threshold is chosen, uniform sampling site is as newborn target in the measurement region
Particle counts the quantitative variation of its corresponding target movement model type respectively, according to the generation probability of initial model, calculates every
The probability of a motion model realizes maneuvering target with tracking (MM-PF-TBD) algorithm before the particle filter detection of multi-model
Detection and tracking under low signal-to-noise ratio.
The specific implementation steps are as follows for it:
Step 1: each receiving station carries out relevant treatment using the reception signal and reference signal of kth sampling instant, using mark
Quasi- space apodization (SVA) technology handles the result of relevant treatment, obtains and measures initial data；
Step 2: measurement model module establishes corresponding measurement mould according to the measurement initial data of corresponding scene and acquisition
Type；
(1) measurement model module initialization scene parameters: signal carrier frequency, the light velocity, the position of each transmitting station SFN, respectively
The position of receiving station, the power loss factor, signal bandwidth, delay resolution, Doppler frequency resolution measure noise component(s) side
Difference.
(2) measurement model module passes through the dbjective state vector of kth sampling instantWhen propagation
Prolong, Doppler frequency, the power loss factor, signal bandwidth and integration time provide the broad sense mould after SVA technical treatment
Paste functionSpecific representation:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency list
The serial number of first lattice, tmAnd ζnIt is the numerical value of propagation delay and Doppler frequency cell respectively, L is the power loss factor, and B is letter
Number bandwidth,WithIt is n-th under kth sampling instant respectivelytA transmitting station to i-th of receiving station propagation delay with
Doppler frequency, ∏2/B() and ∏2/T() is rectangular window function, and T is integration time, and
In upper two formula, fcFor signal carrier frequency, c is the light velocity, pk=(xk,yk)TTarget is represented in the position coordinates of x-axis and y-axis,It is n-thtThe position of a transmitting station,It is the position of i-th of receiving station,Target is represented in x-axis and y
The velocity component of axis, | | | |2For two norms of vector.
(3) measurement model module provides i-th of receiving station and receives at i-th of kth sampling instant according to generalized fuzzy function
The measuring value for the metric data model stoodRepresentation is as follows:
Wherein, m is the serial number of propagation delay cell, NdFor the quantity of propagation delay cell, n is Doppler frequency list
The serial number of first lattice, NξFor the quantity of Doppler frequency cell,For the measurement intensity of resolution cell lattice (m, n).It should
Measure intensityFor the data after relevant treatmentModulus value,Specific representation it is as follows:
In above formula, i is the serial number of receiving station, and m and n are respectively the serial number of propagation delay and Doppler frequency cell, nt
And NtThe respectively serial number and quantity of transmitting station,WithRespectively i-th of receiving station n-thtA transmitting station is adopted at k-th
The range value and phase at sample moment,For i-th of receiving station n-thtThe generalized fuzzy function of a transmitting station,Represent the multiple Gauss partition noise item of resolution cell lattice (m, n).
Step 3: likelihood ratio function module derives spoke outside single frequency network according to measurement model and received measurement initial data
Penetrate source radar system single goal likelihood ratio function:
Wherein,For target average amplitude vector, Lk() is single-frequency
Net external illuminators-based radar single goal likelihood ratio function,For the metric data of all receiving stations, xkFor dbjective state to
Amount, EkIt is target there are variable, i indicates the serial number of receiving station, NrFor the quantity of receiving station,Seemingly for i-th of receiving station
So than function,The metric data of i-th of receiving station is represented,Represent all target echoes of i-th of receiving station
Average amplitude.
(1) the single frequency network external radiation source radar system measurement model that likelihood ratio function module is established according to step 2, it is known that phase
Close treated dataIt is to obey multiple Gauss to be distributed, when targets are present,Modulus valueIn dbjective state vector xkAnd signal amplitudeUnder the conditions of be obey L-S distribution, in the absence of target,It is Rayleigh distributed, utilizes measurement intensityTo replaceObtain i-th of receiving station
MeasurementThe representation of probability density function under the conditions of target exists and is not present with target:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station,Represent the conditional probability of i-th of receiving station
Density function, xkFor dbjective state vector,All targets of i-th of receiving station represented under kth sampling instant are returned
Wave amplitude, EkFor target, there are variables, and when targets are present, which is 1, otherwise is 0,To measure noise component(s) variance,For i-th of receiving station n-thtA transmitting station k-th of sampling instant range value,For i-th of receiving station n-tht
The generalized fuzzy function of a transmitting station, I0() is the modified Bessel function of zeroth order, the measurement of i-th of receiving stationIt must
It must be non-negative value.
(2) under the conditions of the probability density function under target existence condition is not present likelihood ratio function module divided by target
Probability density function, then the cell (m, n) of i-th of receiving station measures likelihood ratio function concrete form:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency list
The serial number of first lattice, NtFor the quantity of transmitting station,Likelihood ratio function is measured for the cell (m, n) of i-th of receiving station,For the metric data of i-th of receiving station, xkFor dbjective state vector,It represents under kth sampling instant
All target echo amplitudes of i-th of receiving station, EkIt is target there are variable,To measure noise component(s) variance,It is i-th
A receiving station n-thtA transmitting station k-th of sampling instant range value,For i-th of receiving station n-thtA transmitting station
Generalized fuzzy function, I0() is the modified Bessel function of zeroth order.
(3) likelihood ratio function module is according to Swerling I and Swerling III target fluctuation model, and each receiving station
Each target echo average amplitudeThe target echo amplitude observedIt is mutual independence between different moments
, i-th of receiving station is provided in the specific expression of the target echo amplitude Swerling target fluctuation model of k-th of sampling instant
Form:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, δ >=0 are relief model parameter, and δ=1 corresponds to
Swerling I type relief model, δ=2 correspond to Swerling type III relief model, and Γ (δ) represents the factorial of δ -1, signal
Amplitude
(4) since fluctuating of the target echo amplitude between the scan period is independent from each other, and each receiving station is each
Paths echo amplitude is also independent from each other.Likelihood ratio function module rises according to Swerling I and Swerling III target
Model is lied prostrate, the likelihood ratio function concrete form indicated with average target echo amplitude is provided:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency list
The serial number of first lattice, NtFor the quantity of transmitting station,Likelihood ratio function is measured for the cell (m, n) of i-th of receiving station,For the metric data of i-th of receiving station, xkFor dbjective state vector,It is all to represent i-th of receiving station
The average amplitude of target echo,All target echo amplitudes of i-th of receiving station under kth sampling instant are represented,
EkIt is target there are variable,For i-th of receiving station n-thtRange value of a transmitting station in k-th of sampling instant.
(5) likelihood ratio function module calculates target likelihood ratio function using the method for Monte Carlo integral, and it is close to provide it
Like representation:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency list
The serial number of first lattice, NtFor the quantity of transmitting station,Likelihood ratio function is measured for the cell (m, n) of i-th of receiving station,For the metric data of i-th of receiving station, xkFor dbjective state vector,It is all to represent i-th of receiving station
The average amplitude of target echo, EkIt is target there are variable,For i-th of receiving station n-thtA transmitting station is in k-th of sampling
The range value at quarter,All target echo amplitudes of i-th of receiving station under kth sampling instant are represented,Represent
ntThe Monte Carlo of a target echo amplitude is counted.
(6) likelihood ratio function module is according to n-th under i-th of receiving stationtThe generalized fuzzy function of a target echoThe region that can be influencedWith represent all areas'sAnd it is each
Resolution cell lattice are all conditional samplings, provide the likelihood ratio function concrete form of i-th of receiving station:
Wherein, m and n is respectively the serial number of propagation delay and Doppler frequency cell,For i-th receiving station
Likelihood ratio function,For the entire metric data in kth sampling instant of i-th of receiving station, xkFor dbjective state vector,Represent the average amplitude of all target echoes of i-th of receiving station, EkFor target, there are variables, when targets are present,
The variable is 1, otherwise is 0, NdFor the quantity of propagation delay cell, NξFor the quantity of Doppler frequency cell,
For the metric data of i-th of receiving station.
(7) likelihood ratio function module is mutual according to the measurement between the likelihood ratio representation of MIMO radar and receiving station
It is independent, it is known that complete likelihood ratio function is exactly the product of the likelihood ratio function of each resolution cell lattice of each receiving station, by
In each generalized fuzzy functionIt is merely able to influence certain block regionOn other resolution cell lattice almost without influence,
So approximate can finally be write as the product of each influence area likelihood ratio function of each receiving station.Then single frequency network external sort algorithm thunder
It can be written as following form up to single goal likelihood ratio function:
Wherein,For target average amplitude vector, Lk() is single-frequency
Net external illuminators-based radar single goal likelihood ratio function,For the metric data of all receiving stations, xkFor dbjective state to
Amount, EkIt is target there are variable, i indicates the serial number of receiving station, NrFor the quantity of receiving station,Seemingly for i-th of receiving station
So than function,The metric data of i-th of receiving station is represented,Represent all target echoes of i-th of receiving station
Average amplitude.
Step 4: algorithm implement module with based on multi-model particle filter detection before tracking (MM-PF-TBD) algorithm come
Realize detection and tracking of the maneuvering target under low signal-to-noise ratio；
(1) it is N that algorithm, which implements the total population of module initialization,pA, the particle that exists is NcA, l is the serial number of particle,
Input -1 sampling instant dbjective state particle of kthThere are variable particles for targetMesh
Mark motion model particleAnd weightWherein, firstWithIt is attached to corresponding mesh
Mark particle
(2) algorithm implements module there are transfer matrix Θ that there are variables to predict kth sampling instant target according to target
(3) to target after above-mentioned prediction, there are variablesDbjective state particle predicted: 1. for newborn mesh
Mark, i.e., Intended particle, since such particle cannot be predicted by target movement model, thus select
The measurement region more than a certain detection threshold is taken, uniform sampling site is used as newborn intended particle, and its corresponding mesh in the region
Mark motion model variableIt is generated according to initial model probability.2. for the particle that exists, i.e.,Pass through
Target movement model transition probability predicts kth sampling instant target movement model variableAccording to acquisitionCorresponding mesh
The mark equation of motion predicts dbjective state particle.
(4) algorithm implements the likelihood ratio function that module is obtained according to likelihood ratio function module, calculates not normalized first
Particle weights
Wherein, Lk() is single frequency network external illuminators-based radar single goal likelihood ratio function,For all receiving stations
Metric data,For first of dbjective state particle of kth sampling instant,For target average amplitude vector, EkFor target presence
Variable,For first of target of kth sampling instant, there are variable particles.
(5) algorithm implements module normalization particle weights:
Wherein, l is the serial number of particle, NpFor number of particles,To normalize particle weights,Not normalize particle
Weight.
(6) algorithm implements module calculating target existing probability:
Wherein, P () represents target existing probability, EkIt is target there are variable,Represent all receiving stations
Metric data, l are the serial number of particle, NpFor number of particles,For first of target of kth sampling instant, there are variable particles.
(7) algorithm implements module estimation dbjective state vector
Wherein, l is the serial number of particle, NpFor number of particles,For first of dbjective state particle of kth sampling instant,For
There are variable particles for first of target of kth sampling instant.
(8) it is directed toDbjective state particle, algorithm implement module count its corresponding target movement model class respectively
The quantity of type, is denoted as R1, R2, then calculate the probability of a motion model:
Wherein, γkFor kth sampling instant target movement model variable,For γk=1 motion model probability,For γk
=2 motion model probability, l are the serial number of particle, NpFor number of particles,For first of target of kth sampling instant, there are variable grains
Son.
(9) in order to solve the problems, such as sample degeneracy, algorithm implementation module carries out re-sampling operations.
Experiment content and result:
Simulated conditions:
Signal carrier frequency fcFor 230MHz, the light velocity is 3 × 1081 position of the transmitting station m/s, SFNAt (0km, 60km)T, SFN
2 position of transmitting stationAt (43km, 43km)T, 3 position of the transmitting station SFNAt (60km, 0km)T, 1 position of receiving station?
(0km,0km)T, 2 position of receiving stationAt (5km, -5km)T, power loss factor L is 0.8, and signal bandwidth B is 1.536MHz,
Integration time T is 39.872ms, delay resolution ΔdFor 0.65us, Doppler frequency resolution ΔζFor 12.54Hz, measurement is made an uproar
Sound component varianceIt is 1.Only one maneuvering target in setting monitoring region, the target occur being uniform rectilinear since 7s
Operation, original state (25km, 25m/s, 20km, 20m/s)T, at the uniform velocity turning motion, rate of turn are done between 17-21s
ω=9 °/s, 22s continues to do linear uniform motion until 33s disappears.Sampling interval duration TsFor 1s, total observation time is 40s.
Intended particle number is 6000, target probability of death PdeathWith newborn probability PbirthIt is 0.05.The transfer of target movement model
Matrix ΘMAre as follows:
Define the n-th of i-th of receiving stationtThe signal-to-noise ratio of echo path signalAre as follows:
Refering to the corresponding experimental result picture of Fig. 3 to Fig. 7.In order to verify effectiveness of the invention more fully hereinafter, now it is based on
The above simulated conditions do following experiment:
Experiment 1: under the scene of Liang Ge receiving station, for different signal-to-noise ratio, the proving and comparisom present invention is to target detection
With the performance of tracking.Fig. 3 is maneuvering target existing probability comparison diagram under different signal-to-noise ratio, and Fig. 4 is maneuvering target position RMSE
The comparison diagram under different signal-to-noise ratio, Fig. 5 are maneuvering target motion model probability Estimation figures.
Experiment 2: for the receiving station of different number, in the case where signal-to-noise ratio is 5dB situation, the proving and comparisom present invention examines target
Survey the performance with tracking.Fig. 6 is maneuvering target existing probability comparison diagram under different receiving station's quantity, and Fig. 7 is maneuvering target position
Set RMSE comparison diagram under different receiving station's quantity.
Above in conjunction with attached drawing to the present invention have been described in detail, it is to be noted that being described in examples detailed above
Preferred embodiment only of the invention, is not intended to restrict the invention, and for those skilled in the art, the present invention can
To there is various modifications and variations, all within the spirits and principles of the present invention, any modification, equivalent replacement, improvement and so on,
It should be included within scope of the presently claimed invention.
Claims (21)
1. detection and the tracking of a kind of multi-receiver station single frequency network external illuminators-based radar maneuvering target, it is characterised in that including such as
Lower step: under target echo amplitude scintillation environment, each receiving station using kth sampling instant reception signal and reference signal into
Row relevant treatment is handled the result of relevant treatment using normed space apodization (SVA) technology, is obtained and is measured original number
According to；It being tracked under frame before the detection based on particle filter, measurement model module receives the initial data after relevant treatment, according to
The measurement initial data of corresponding scene and acquisition, provides the generalized fuzzy function after SVA technical treatment, establishes receiving station
Metric data model, be applied to subsequent likelihood ratio function module and algorithm and implement module；Likelihood ratio function module is according to amount
Measured data model and the received single goal likelihood ratio function for measuring initial data and providing the cell (m, n) of i-th of receiving station,
For Swerling I and Swerling III target fluctuation model, target likelihood ratio is calculated using the method for Monte Carlo integral
Function derives single goal according to the likelihood ratio function representation form of the mutual independence measured and MIMO radar between receiving station
Likelihood ratio function；Algorithm implements module, and according to target, there are transfer matrix Θ that predict kth sampling instant target, there are variables
The measurement region more than a certain detection threshold is chosen, uniform sampling site counts respectively as newborn intended particle in the measurement region
The quantitative variation of its corresponding target movement model type calculates each motion model according to the generation probability of initial model
Probability realizes maneuvering target under low signal-to-noise ratio with tracking (MM-PF-TBD) algorithm before the particle filter detection of multi-model
Detection and tracking.
2. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
It is characterized in that: in multi-receiver station single frequency network external illuminators-based radar scene, containing NtA transmitting station SFN and NrA receiving station, each
After reference signal purification first restores in receiving station, place related to the reception signal progress after direct path cancellation and clutter recognition
Reason does not do any thresholding operation excessively to the data after relevant treatment, finally unifies to be sent into data fusion center, as subsequent measurement
Model module, likelihood ratio function module and algorithm implement the data sample that module implements operation.
3. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: measurement model module establishes corresponding measurement model according to the measurement initial data of corresponding scene and acquisition.
4. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: measurement model module passes through the dbjective state vector of kth sampling instantPropagation delay,
Doppler frequency, the power loss factor, signal bandwidth and integration time, provide the generalized fuzzy after SVA technical treatment
FunctionSpecific representation:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency list
The serial number of first lattice, tmAnd ζnThe numerical value of propagation delay and Doppler frequency cell respectively, L be power loss because
Son, B are signal bandwidth,WithIt is n-th under kth sampling instant respectivelytA transmitting station is to i-th of receiving station
Propagation delay and Doppler frequency, Π2/B() and ∏2/T() is rectangular window function, and T is integration time, and
In upper two formula, fcFor signal carrier frequency, c is the light velocity, pk=(xk,yk)TTarget is represented in the position coordinates of x-axis and y-axis,
It is n-thtThe position of a transmitting station,It is the position of i-th of receiving station,Target is represented in x-axis and y-axis
Velocity component, | | | |2For two norms of vector.
5. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: measurement model module provides i-th of receiving station in i-th of receiving station of kth sampling instant according to generalized fuzzy function
Metric data model measuring valueRepresentation is as follows:
Wherein, m is the serial number of propagation delay cell, NdFor the quantity of propagation delay cell, n is Doppler frequency cell
Serial number, NξFor the quantity of Doppler frequency cell,For the measurement intensity of resolution cell lattice (m, n)；The measurement
IntensityFor the data after relevant treatmentModulus value,Specific representation it is as follows:
In above formula, i is the serial number of receiving station, and m and n are respectively the serial number of propagation delay and Doppler frequency cell, ntAnd NtPoint
Not Wei transmitting station serial number and quantity,WithRespectively i-th of receiving station n-thtA transmitting station is in k-th of sampling instant
Range value and phase,For i-th of receiving station n-thtThe generalized fuzzy function of a transmitting station,It represents
The multiple Gauss partition noise item of resolution cell lattice (m, n).
6. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module derives single frequency network external sort algorithm thunder according to measurement model and received measurement initial data
Up to system single goal likelihood ratio function.
7. multi-receiver station single frequency network external illuminators-based radar as described in claim 1 is to the detecting and tracking method of maneuvering target,
Be characterized in that: likelihood ratio function module is according to the single frequency network external radiation source radar system measurement model of foundation, after relevant treatment
DataMultiple Gauss distribution is obeyed, when targets are present,Modulus valueDbjective state to
Measure xkAnd signal amplitudeUnder the conditions of obey L-S distribution, in the absence of target,Rayleigh distributed.
8. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module utilizes measurement intensityTo replaceObtain i-th of receiving station
It measuresThe representation of probability density function under the conditions of target exists and is not present with target:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station,Represent the conditional probability density letter of i-th of receiving station
Number, xkFor dbjective state vector,All target echo amplitudes of i-th of receiving station under kth sampling instant are represented,
EkFor target, there are variables, and when targets are present, which is 1, otherwise is 0,To measure noise component(s) variance,It is i-th
A receiving station n-thtA transmitting station k-th of sampling instant range value,For i-th of receiving station n-thtA transmitting station
Generalized fuzzy function, I0() is the modified Bessel function of zeroth order, the measurement of i-th of receiving stationIt is non-negative
Value.
9. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module the probability density function under target existence condition is not present divided by target under the conditions of it is general
Rate density function, then the cell (m, n) of i-th of receiving station measures likelihood ratio function concrete form:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency cell
Serial number, NtFor the quantity of transmitting station,Likelihood ratio function is measured for the cell (m, n) of i-th of receiving station,For the metric data of i-th of receiving station, xkFor dbjective state vector,It represents under kth sampling instant
All target echo amplitudes of i-th of receiving station, EkIt is target there are variable,To measure noise component(s) variance,It is i-th
A receiving station n-thtA transmitting station k-th of sampling instant range value,For i-th of receiving station n-thtA transmitting station
Generalized fuzzy function, I0() is the modified Bessel function of zeroth order.
10. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module is according to Swerling I and Swerling III target fluctuation model, and each receiving station
The average amplitude of each target echoThe target echo amplitude observedIt is independent of each other between different moments,
I-th of receiving station is provided in the specific expression shape of the target echo amplitude Swerling target fluctuation model of k-th of sampling instant
Formula:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, δ >=0 are relief model parameter, and δ=1 corresponds to Swerling
I type relief model, δ=2 correspond to Swerling type III relief model, and Γ (δ) represents the factorial of δ -1, signal amplitude
11. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module is provided according to Swerling I and Swerling III target fluctuation model with average mesh
Mark the likelihood ratio function concrete form that echo amplitude indicates:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency cell
Serial number, NtFor the quantity of transmitting station,Likelihood ratio function is measured for the cell (m, n) of i-th of receiving station,For the metric data of i-th of receiving station, xkFor dbjective state vector,It is all to represent i-th of receiving station
The average amplitude of target echo,All target echo amplitudes of i-th of receiving station under kth sampling instant are represented,
EkIt is target there are variable,For i-th of receiving station n-thtRange value of a transmitting station in k-th of sampling instant.
12. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module calculates target likelihood ratio function using the method for Monte Carlo integral, and provides its approximation
Representation:
Wherein, i and ntThe respectively serial number of receiving station and transmitting station, m and n are respectively propagation delay and Doppler frequency cell
Serial number, NtFor the quantity of transmitting station,Likelihood ratio function is measured for the cell (m, n) of i-th of receiving station,For the metric data of i-th of receiving station, xkFor dbjective state vector,It is all to represent i-th of receiving station
The average amplitude of target echo, EkIt is target there are variable,For i-th of receiving station n-thtA transmitting station is in k-th of sampling
The range value at quarter,All target echo amplitudes of i-th of receiving station under kth sampling instant are represented,It represents
N-thtThe Monte Carlo of a target echo amplitude is counted.
13. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: likelihood ratio function module is according to n-th under i-th of receiving stationtThe generalized fuzzy function of a target echo
The region that can be influencedWith represent all areas'sAnd each resolution cell
Lattice are all conditional samplings, provide the likelihood ratio function concrete form of i-th of receiving station:
Wherein, m and n is respectively the serial number of propagation delay and Doppler frequency cell,For the likelihood of i-th of receiving station
Than function,For the entire metric data in kth sampling instant of i-th of receiving station, xkFor dbjective state vector,Represent the average amplitude of all target echoes of i-th of receiving station, EkFor target, there are variables, when targets are present,
The variable is 1, otherwise is 0, NdFor the quantity of propagation delay cell, NξFor the quantity of Doppler frequency cell,
For the metric data of i-th of receiving station.
14. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: it is N that algorithm, which implements the total population of module initialization,pA, the particle that exists is NcA, l is the serial number of particle, defeated
Enter -1 sampling instant dbjective state particle of kthThere are variable particles for targetTarget
Motion model particleAnd weightWherein, firstWithIt is attached to corresponding target
Particle
15. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: algorithm implements module, and according to target, there are transfer matrix Θ that predict kth sampling instant target, there are variablesIt is right
There are variables for target after predictionDbjective state particle predicted: 1. for newborn target, i.e.,
Intended particle, cannot be predicted by target movement model for class particle, choose be more than a certain detection threshold measurement
Region, in the region, uniform sampling site is used as newborn intended particle, and its corresponding target movement model variableAccording to initial
Model probability generates；2. for the particle that exists, i.e.,It is predicted by target movement model transition probability
Kth sampling instant target movement model variableAccording to acquisitionCorresponding moving equation is to dbjective state particle
It is predicted.
16. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: algorithm implements the likelihood ratio function that module is obtained according to likelihood ratio function module, calculates not normalized first
Sub- weight
Wherein, Lk() is single frequency network external illuminators-based radar single goal likelihood ratio function,For the amount of all receiving stations
Measured data,For first of dbjective state particle of kth sampling instant,For target average amplitude vector, EkExist for target and becomes
Amount,For first of target of kth sampling instant, there are variable particles.
17. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: algorithm implements module normalization particle weights:
Wherein, l is the serial number of particle, NpFor number of particles,To normalize particle weights,Not normalize particle weights.
18. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: algorithm implements module and calculates target existing probability:
Wherein, P () represents target existing probability, EkIt is target there are variable,Represent the measurement of all receiving stations
Data, l are the serial number of particle, NpFor number of particles,For first of target of kth sampling instant, there are variable particles.
19. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
Be characterized in that: algorithm implements module estimation dbjective state vector
Wherein, l is the serial number of particle, NpFor number of particles,For first of dbjective state particle of kth sampling instant,For kth
There are variable particles for first of target of sampling instant.
20. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
It is characterized in that: being directed toDbjective state particle, algorithm implement module count its corresponding target movement model type respectively
Quantity, be denoted as R1, R2, then calculate the probability of a motion model:
Wherein, γkFor kth sampling instant target movement model variable,For γk=1 motion model probability,For γk=2
Motion model probability, l are the serial number of particle, NpFor number of particles,For first of target of kth sampling instant, there are variable particles.
21. detection and the tracking of multi-receiver station single frequency network external illuminators-based radar maneuvering target as described in claim 1,
It is characterized in that: in order to solve the problems, such as that sample degeneracy, algorithm implement module and carry out re-sampling operations.
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