CN106772299A - A kind of PD radar weak target Dynamic Programming detection methods based on distance matching - Google Patents

Abstract

The invention discloses a kind of PD radar weak target Dynamic Programming detection methods based on distance matching, belong to radar dim target detection tracking field.PD radars generally use medium-PRF high, and fuzzy so as to cause distance by radar to measure, particularly under the low signal-to-noise ratio environment including clutter and noise, the test problems of PD radar weak targets just become more complicated.A kind of thought of the present invention based on tracking before detection, it is proposed that PD radar weak target Dynamic Programming detection methods based on distance matching.First, the temporal correlation that objective fuzzy is measured is recovered by apart from matching treatment；Then, using the characteristic of Dynamic Programming batch processing, realize the non-inherent accumulation to weak target information, the target missing inspection for avoiding PD radar range findings fuzzy problem and weak target low signal-to-noise ratio characteristic and causing, detection probability of the PD radars to weak target can be effectively improved, Project Realization easily, there is stronger practicality and application value.

Description

A kind of PD radar weak target Dynamic Programming detection methods based on distance matching

Technical field

The invention belongs to track field before radar dim target detection, it is adaptable to which it is right under PD radar range finding hazy conditions to solve The integration detection problem of weak target.

Background technology

The detecting and tracking of radar weak target is a difficult and vital problem, for winning following high-tech War has decisive significance.With the development of stealth technology, the appearance of weak target proposes huge challenge to radar performance, Because the echo-signal signal to noise ratio of weak target is very low, target can not be realized with traditional short time phase-coherent accumulation detection method Reliable detection；On the other hand, in order to unambiguously measure target velocity, PD radars are generally using high, medium-PRF Pattern, so as to the range measurement for causing target is fuzzy, this causes that PD radars become more to the test problems of weak target It is difficult.

In order to improve target signal to noise ratio, it is necessary to carry out long-time non-inherent accumulation to obtain more signal energies, wherein Most typical method is dynamic programming method.Dynamic Programming is that a kind of equivalent exhaustive search based on multistage process decision is calculated Method, it is classified treatment and one multi-dimensional optimization search problem is divided into several one-dimensional Optimizing Search problems by the multistage, most The accumulation of multiframe measurement information is realized in the planning process in good path, so as to improve target signal to noise ratio, radar weak target is realized Reliable detection；But, when ambiguity occur in PD radar measurements, it is interrupted that target is measured in time-space relationship, it is impossible to adopted Long-time non-inherent accumulation is carried out to the backward energy from same target with dynamic programming method.

In document, [Zhang Wei, hole makes and saying, a kind of improvement DPA Faint target detection algorithms for HPRF radars of the such as Yang Xiaobo [J] modern radars .2011,33 (5)] in, author proposes a kind of improved dynamic programming method to solve the above problems, its Basic step is as follows：

1) current time is measured using the predicted state of last moment carries out saltus step judgement, judges measure whether cross-module is pasted It is interval；

2) according to court verdict, different Dynamic Programming search strategies are selected, realizes the accumulation of target energy；

3) by Threshold detection, the backtracking of objective fuzzy flight path is obtained.

The above method judges to take different search strategies by saltus step, so as to realize along the non-phase of objective fuzzy flight path Ginseng accumulation, to improve target signal to noise ratio, realizes the detection to objective fuzzy flight path, but it has the disadvantage that：

1) need to carry out saltus step judgement according to the predicted state of last moment, it is therefore desirable to the priori letter of dbjective state model Breath, when that cannot set up dbjective state model or model sets up inaccurate, may result in misjudgment；

2) by being only able to detect the fuzzy flight path of target after algorithm process, it is impossible to fundamentally solve the inspection of radar target Survey tracking problem；

If 3) to obtain the true flight path of target, in addition it is also necessary to carry out ambiguity solution treatment, the solution of algorithm by remainder theorem Fuzzy performance is limited by remainder theorem application conditions.

The content of the invention

1. the technical problem to be solved

The purpose of the present invention is to propose to it is a kind of based on distance matching PD radar weak target Dynamic Programming detection methods, from Fundamentally solve the problems, such as that dynamic programming method cannot be applied to PD radar ambiguities.

2. technical scheme

The invention provides a kind of PD radar weak target Dynamic Programming detection methods based on distance matching, using technology Protocol step is as follows：

Step 1：Initialization system parameter：

P_faIt is initial threshold false-alarm；

R_maxIt is maximum radar range；

M is the species of PD radar pulse repetition frequencies；

M=1,2 ..., M is the sequence number of pulse recurrence frequency；

F_mIt is m-th pulse recurrence frequency；

Ru_mIt is pulse recurrence frequency F_mCorresponding maximum unam；

K is the scanning moment sum of processing data；

K=1,2 ..., K is the scanning moment sequence number of data；

N_kFor the total number that the k moment measures；

N=1,2 ..., N_kFor the sequence number that the k moment measures；

It is n-th fuzzy distance of measurement of k moment；

It is n-th orientation of measurement of k moment；

A_nK () is n-th echo amplitude of measurement of k moment；

It is n-th measurement unit of k moment；

Z′_kIt is k moment distance matching measurement sequence；

J_kIt is the total number of k moment potential tracks；

J=1,2 ..., J_kIt is the sequence number of k moment potential tracks；

S_kJ () is the object function of j-th potential track of k moment；

I_kJ () is the cost function of j-th potential track of k moment；

H (j) is the j-th target actual measurements sequence for eventually detecting；

Step 2：Initial threshold treatment

Scanning moment k, by the echo data of each range-azimuth unit respectively with an initial threshold η₁Compared Compared with, thus eliminate partial noise influence, obtain the metric data sequence Z after PD radar initial detectings_k, concrete measure is：

(1) in scanning moment k, by each measurement unit z_n(k) respectively with an initial threshold η₁It is compared, so that Eliminate partial noise influence；In order to retain target information to greatest extent, a larger initial false-alarm, its initial threshold is selected to set It is set to：

Wherein,It is Z_kIn in all measurement units amplitude average value：

(2) initial detecting is carried out to radar measurement data using initial threshold, the radar measurement sequence after being processedWherein z_nK () value is as follows：

In subsequent treatment, the measurement unit that value is all 0 is skipped, in this manner it is possible to the influence of exclusive segment noise, from And reduce the data volume of computer disposal；

Step 3：Apart from matching treatment

Measurement of the PD radars to target range is typically obscured, and the measurement of all distance dimensions is all compressed in the 1st and obscures In interval, target is caused to measure discontinuous on space-time, therefore, it is difficult to carry out non-inherent accumulation along targetpath；In order to solve This problem, to radar measurement sequence Z_kEnter row distance matching treatment, by distanceEquiprobability matches all fuzzy intervals, Obtain the distance matching measurement sequence Z ' of radar_k, so as to recover the temporal correlation of metric data, concrete measure is：

(1) each pulse recurrence frequency F is calculated_mFuzzy interval number Φ_m

Φ_m=Int (R_max/Ru_m),

Wherein Int () represents rounding operation, m=1,2 ..., M；

(2) the k moment, F is passed through for radar_mThe n-th measurement z for measuring_n(k), by its distanceMatch l_mIndividual mould Paste is interval, can obtain a matching distance

(3) corresponding orientation is addedEcho amplitude A_n(k), fuzzy interval valued numbers l_mEtc. information, a distance matching is built Measure

(4) fuzzy interval valued numbers l is made_mIn l_m=1,2 ..., Φ_mIn continuous value, obtain one group of distance matching and measure

(5) order measures number n in n=1,2 ..., N_kIn continuous value, the distance matching for obtaining all measurements of k moment measures Sequence Z '_k

So, by apart from matching treatment, in Z '_kThe middle temporal correlation for having recovered metric data；

(6)Z′_kFor a line number is Φ_mN_kSequence, be simplified shown as：

Wherein, i represents distance matching measurement sequence Z '_kIn measurement line number, i=1,2 ..., Φ_mN_k, z '_iK () represents Z′_kIn the i-th row distance matching measure vector；

By above-mentioned treatment, range-to-go matching measurement sequence Z ' is obtained_k, because processing procedure is to all moulds of radar Paste the equiprobability matching of interval row；Therefore distance matching measurement necessarily falls in target realistic blur interval；It can be seen that, after treatment Measurement sequence Z '_kIn certainly exist the actual measurements of target, so as to recover the temporal correlation of metric data.

Step 4：Dynamic Programming is processed

All distance matching measurement sequences that K scanning moment treatment is obtained are constituted into a search set Z '_1:K= [Z′₁,Z′₂,…,Z′_K]；After above-mentioned steps 3 are apart from matching treatment, Z '_kIn certainly exist the actual distance at target k moment Measure, thus recovered the temporal correlation of metric data；And then understand, the true flight path of target is inevitable and is uniquely present in Search set Z '_1:KIn；In the case where error in measurement is not considered, the target measurement of different pulse repetition should not in the same time This is accumulated on its true flight path, and random noise is measured and do not possess above-mentioned accumulation then；Therefore, it can by Dynamic Programming side Method is scanned for all potential tracks in search set, and recursion asks for object function S_k(j) and respective value function I_k(j), Non-inherent accumulation of the echo amplitude along targetpath is realized, so as to improve target signal to noise ratio, radar detedtion probability is improved；

Step 5：Threshold detection

According to the cost function I of accumulation_KJ (), sets detection threshold G, eliminate the relatively low potential track of accumulation value, retains Cost function exceedes the potential track of detection threshold, is deposited into candidate's flight path set D：

D=j | [I_K(j) >=G, j=1,2 ..., J_k],

Wherein, j | [I_K(j) >=G, j=1,2 ..., J_k] represent to extract and all meet I_KThe potential track of (j) >=G conditions Sequence number j；

Step 6：Objective extraction

For each potential track sequence number j ∈ D in candidate's flight path set D, since the k-th moment, backward is carried out Backtracking, obtains target in k moment corresponding measurement sequence number i_k：

i_k=S_k(j)；

From Z '_KMiddle extraction sequence number i_kCorresponding target is measured

Work as k=1 ..., k ..., target corresponding measurement sequence is obtained during K So as to realize the extraction to target real trace.

3. beneficial effect

Compared with background technology, beneficial effects of the present invention explanation：

(1) a kind of PD radar weak target Dynamic Programming detection methods based on distance matching that the present invention is used, can be with Fundamentally solve the problems, such as dim target detection of the PD radars in the case where ambiguity is measured：Recover by apart from matching treatment first The temporal correlation that objective fuzzy is measured, then realizes the non-inherent accumulation to weak target information using Dynamic Programming, it is to avoid The target missing inspection that PD radar range findings fuzzy problem and weak target low signal-to-noise ratio characteristic are caused, can effectively improve PD radars to micro- The detection probability of weak signal target, Project Realization easily, there is stronger practicality and application value.

(2) compared with the conventional method, the present invention need not carry out saltus step judgement, but by apart from matching treatment extracted amount Correlation is surveyed, so as to the recursion for realizing Dynamic Programming is accumulated, processing method is simpler flexibly；On the other hand, existing method It is the blurring trajectorie for detecting target, in addition it is also necessary to which carrying out ambiguity solution treatment by remainder theorem can just obtain the true rail of target Mark, the matching that target actual measurements are realized apart from matching treatment process of the invention, therefore final detection result output be The real trace of target.

Brief description of the drawings

Accompanying drawing 1 is overall flow figure of the invention；

Accompanying drawing 2 is radar measurement figure after initial threshold before processing in the embodiment of the present invention；

Accompanying drawing 3 be in the embodiment of the present invention after matching treatment radar measurement figure；

Accompanying drawing 4 is Dynamic Programming search track plot in the embodiment of the present invention；

Accompanying drawing 5 is the cost function accumulation figure of each search flight path in the embodiment of the present invention；

Accompanying drawing 6 is the schematic diagram that cost function in the embodiment of the present invention to accumulating carries out Threshold detection；

Accompanying drawing 7 is the final target trajectory figure for extracting in the embodiment of the present invention；

Specific embodiment

Main adopting of the invention is experimentally verified that all steps, conclusion are all verified just on Matlab2010a Really.

Embodiment condition：Emulated for a general single goal moving scene.Assuming that target is done in x-y plane Linear uniform motion, its initial position is (51km, 22km), and speed is (- 220m/s, 230m/s), and target is non-fluctuating target, Its radar cross section 0.1m².Sensor parameters are set to：Two coordinate PD radars are in origin of coordinates position, and the scan period is 1s, Maximum operating range is 70km, transmission power P_t=5kw, carrier wavelength lambda=0.1m, radar antenna gain 34dB, radar away from High Resolution is 150m, and angular resolution is 0.5 °.Radar is taken turns to operate using the transmitting pulse of 3 difference PRF, each PRF points Not Wei 16000Hz, 111400Hz and 119400Hz, correspondence maximum unam be respectively Ru₁=9375m, Ru₂=13158m, Ru₃=7732m.It is the situation of 7dB for target signal to noise ratio, the data for emulating 25 scanning moment are verified, wherein initially False-alarm is set to P_fa=10^-2。

Below in conjunction with Figure of description 1 to a kind of PD radar weak target Dynamic Programmings based on distance matching of the invention Detection method is described in detail.With reference to Figure of description 1, handling process of the invention point following steps：

Step 1：Parameter initialization is carried out according to simulated conditions：

P_fa=0.01, R_max=70km, M=3；

F₁=16000Hz, F₂=11400Hz, F₃=19400Hz；

Ru₁=9375m, Ru₂=13158m, Ru₃=7732m, K=25；

N_kIt is the total number for measuring；J_kIt is the total number of potential track；

A_n(k)=[], Z '_k=[]；

S_k(j)=[], I_k(j)=[], H (j)=[], j=1,2 ..., J_k；

Step 2：Initial threshold treatment

(1) in scanning moment k, by each measurement unit z_n(k) respectively with an initial threshold η₁It is compared, so that Eliminate partial noise influence；In order to retain target information to greatest extent, a larger initial false-alarm, its initial threshold is selected to set It is set to：

Wherein,It is Z_kIn in all measurement units amplitude average value：

(2) initial detecting is carried out to radar measurement data using initial threshold, the radar measurement sequence after being processedWherein z_nK () value is as follows：

In subsequent treatment, the measurement unit that value is all 0 is skipped, in this manner it is possible to the influence of exclusive segment noise, from And reduce the data volume of computer disposal.

Fig. 2 show radar measurement comparison diagram after initial threshold before processing, with Fig. 2 (a) contrasts as can be seen that in Fig. 2 (b) Data volume is reduced a lot, but is derived from the data of target and is not filtered out；It can be seen that, the main purpose of initial threshold treatment is By setting a false-alarm thresholding higher, filter out a part of unrelated noise and measure, so as to reduce data volume, improve algorithm Efficiency.

Step 3：Apart from matching treatment

Figure it is seen that what measurement of the PD radars to target range was typically obscured, the measurement of all distance dimensions is all pressed Contracting causes target to measure discontinuous on space-time, therefore, it is difficult to carry out non-coherent along targetpath in the 1st fuzzy interval Accumulation；In order to solve this problem, to radar measurement sequence Z_kEnter row distance matching treatment, by distanceEquiprobability is matched All fuzzy intervals, obtain the distance matching measurement sequence Z ' of radar_k, so that recover the temporal correlation of metric data, specifically Measure is：

(1) each pulse recurrence frequency F is calculated_mFuzzy interval number Φ_m

Φ_m=Int (R_max/Ru_m),

Wherein Int () represents rounding operation, m=1,2 ..., M；

(2) the k moment, F is passed through for radar_mThe n-th measurement z for measuring_n(k), by its distanceMatch l_mIndividual mould Paste is interval, can obtain a matching distance

(3) corresponding orientation is addedEcho amplitude A_n(k), fuzzy interval valued numbers l_mEtc. information, a distance matching is built Measure

(4) fuzzy interval valued numbers l is made_mIn l_m=1,2 ..., Φ_mIn continuous value, obtain one group of distance matching and measure

(5) order measures number n in n=1,2 ..., N_kIn continuous value, the distance matching for obtaining all measurements of k moment measures Sequence Z '_k

So, by apart from matching treatment, in Z '_kThe middle temporal correlation for having recovered metric data；

(6)Z′_kFor a line number is Φ_mN_kSequence, be simplified shown as：

Wherein, i represents distance matching measurement sequence Z '_kIn measurement line number, i=1,2 ..., Φ_mN_k, z '_iK () represents Z′_kIn the i-th row distance matching measure vector；Due to having been carried out to all fuzzy intervals of radar apart from matching treatment process etc. Probability match, therefore necessarily there is distance matching measurement to fall in target realistic blur interval；It can be seen that, the measurement sequence after treatment Z′_kIn certainly exist the actual measurements of target, so as to recover the temporal correlation of metric data；

Fig. 3 show the radar measurement figure after matching treatment, has gone out the reality of target trajectory in figure with white wire collimation mark Region；By radar in Fig. 3 and Fig. 2 it is original measure contrast as can be seen that the distance range measured after apart from matching treatment by One not fuzzy distance match radar maximum measure distance scope；If not examining error in measurement influence, after apart from matching treatment, Mixed and disorderly blur measurement is all accumulated near target real trace in figure 3 in fig. 2, and this explanation can be effective apart from matching treatment Extract the temporal and spatial correlations information that target is measured.

Step 4：Dynamic Programming is processed

Although from figure 3, it can be seen that the correlation of radar measurement is recovered after apart from matching treatment；But by It is relatively low in target signal to noise ratio, if showing target trajectory not over white wire collimation mark, it is difficult to target is distinguished from background Come.In order to solve this problem, target is measured using dynamic programming method next carries out non-inherent accumulation, so as to improve mesh Mark signal to noise ratio, target trajectory is detected from noise background, and specific method is as follows：

All distance matching measurement sequences that K scanning moment treatment is obtained are constituted into a search set Z '_1:K= [Z′₁,Z′₂,…,Z′_K]；After above-mentioned steps 3 are apart from matching treatment, Z '_kIn certainly exist the actual distance at target k moment Measure, thus recovered the temporal correlation of metric data；And then understand, the true flight path of target is inevitable and is uniquely present in Search set Z '_1:KIn；In the case where error in measurement is not considered, the measurement of different pulse repetition should not accumulated in the same time Tire out on true flight path.

The all potential tracks in search set are scanned for by dynamic programming method, search is owned in being gathered Potential track, as shown in Figure 4；Then, recursion asks for the cost function I of each potential track_kJ (), realizes echo amplitude along mesh The non-inherent accumulation of flight path is marked, so as to improve target signal to noise ratio, radar detedtion probability is improved, the cost function of each potential track is such as Shown in Fig. 5.

Step 5：Threshold detection

As shown in fig. 6, according to the cost function I of accumulation_KJ (), setting normalization detection threshold G=45, eliminates accumulation valency The relatively low potential track of value, reserve value function exceedes the potential track of detection threshold, is deposited into candidate's flight path set D：

D=j | [I_K(j) >=G, j=1,2 ..., J_k],

From fig. 6, it can be seen that in embodiments of the present invention, only one of which element D={ j=30 } in candidate's flight path set D；

Step 6：Objective extraction

For the potential track j=30 in candidate's flight path set D, since the k-th moment, backward backtracking is carried out, obtained Target is in k moment corresponding measurement sequence number i_k：

i_k=S_k(30)；

From Z '_KIt is middle to extract corresponding matching measurement

Work as k=1 ..., k ..., target corresponding measurement sequence is obtained during K So as to realize the extraction to target real trace, as shown in Figure 5；From figure 5 it can be seen that the method for the present invention can effectively disappear Except noise Interference Detection to target real trace.

Be can be seen that from embodiment the result and measured instant invention overcomes weak target under PD radar range finding hazy conditions The problem that data cannot be accumulated effectively；Recover the temporal correlation that objective fuzzy is measured by apart from matching treatment, then utilize Dynamic Programming realized to the non-inherent accumulation of weak target information, it is to avoid PD radar range findings fuzzy problem and the low letter of weak target The target missing inspection that ratio characteristic of making an uproar is caused, can effectively improve detection probability of the PD radars to weak target；Under current simulated conditions, The present invention has detection performance well to weak target, while the real trace of target can be directly extracted, without carrying out again Ambiguity solution treatment, Project Realization easily, there is stronger practicality and application value.

Claims (3)

1. it is a kind of based on apart from the PD radar weak target Dynamic Programming detection methods for matching, it is characterised in that including following step Suddenly：

Step 1：Initialization system parameter：

P_faIt is initial threshold false-alarm；

R_maxIt is maximum radar range；

M is the species of PD radar pulse repetition frequencies；

M=1,2 ..., M is the sequence number of pulse recurrence frequency；

F_mIt is m-th pulse recurrence frequency；

Ru_mIt is pulse recurrence frequency F_mCorresponding maximum unam；

K is the scanning moment sum of processing data；

K=1,2 ..., K is the scanning moment sequence number of data；

N_kFor the total number that the k moment measures；

N=1,2 ..., N_kFor the sequence number that the k moment measures；

It is n-th fuzzy distance of measurement of k moment；

It is n-th orientation of measurement of k moment；

A_nK () is n-th echo amplitude of measurement of k moment；

It is n-th measurement unit of k moment；

Z′_kIt is k moment distance matching measurement sequence；

J_kIt is the total number of k moment potential tracks；

J=1,2 ..., J_kIt is the sequence number of k moment potential tracks；

S_kJ () is the object function of j-th potential track of k moment；

I_kJ () is the cost function of j-th potential track of k moment；

H (j) is the j-th target actual measurements sequence for eventually detecting；

Step 2：Initial threshold treatment

Scanning moment k, by the echo data of each range-azimuth unit respectively with an initial threshold η₁It is compared, from And partial noise influence is eliminated, obtain the metric data sequence Z after PD radar initial detectings_k；

Step 3：Apart from matching treatment

Measurement of the PD radars to target rangeTypically fuzzy, cause target to measure discontinuous on space-time therefore difficult Non-inherent accumulation is carried out with along targetpath；In order to solve this problem, to radar measurement sequence Z_kEnter at row distance matching Reason, willEquiprobability matches all fuzzy intervals, obtains the distance matching measurement sequence Z ' of radar_k, so as to recover to measure The temporal correlation of data；

Step 4：Dynamic Programming is processed

All distance matching measurement sequences that K scanning moment obtains are constituted into a search set Z '_1:K=[Z '₁,Z′₂,…, Z′_K], all potential tracks in search set are scanned for by dynamic programming method, recursion asks for object function S_k(j) With respective value function I_kJ (), realizes non-inherent accumulation of the echo amplitude along targetpath；

Step 5：Threshold detection

In k=K, according to the cost function I of accumulation_KJ (), sets detection threshold G, eliminate the relatively low potential track of accumulation value, Reserve value function exceedes the potential track of detection threshold, is deposited into candidate's flight path set D：

D=j | [I_K(j) >=G, j=1,2 ..., J_k],

Wherein, j | [I_K(j) >=G, j=1,2 ..., J_k] represent to extract and all meet I_KThe sequence of the potential track of (j) >=G conditions Number j；

Step 6：Objective extraction

For each potential track sequence number j ∈ D in candidate's flight path set D, since the k-th moment, backward backtracking is carried out, Target is obtained in k moment corresponding measurement sequence number i_k：

i_k=S_k(j)；

From Z '_KMiddle extraction sequence number i_kCorresponding matching is measuredWork as k=1 ..., k ..., the measurement sequence of target is obtained during KSo as to realize the extraction to target real trace.

2. it is according to claim 1 it is a kind of based on distance matching PD radar weak target Dynamic Programming detection methods, its It is characterised by the initial threshold processing method described in step 2：

S21：In scanning moment k, by each measurement unit z_n(k) respectively with an initial threshold η₁It is compared, so as to eliminate Partial noise influences；In order to retain target information to greatest extent, a larger initial false-alarm, the setting of its initial threshold are selected For：

${\mathrm{\η}}_1=-ln\left(P_{fa}\right)\·\stackrel{\‾}{A},$

Wherein, natural logrithm is sought in ln () expressions,It is k moment all measurement unit z_nThe average value of amplitude in (k), n=1, 2,…,N_k：

$\stackrel{\‾}{A}=\frac{1}{N_k}\underset{n=1}{\overset{N_k}{\Σ}}{A}_n\left(k\right),$

Wherein, ∑ [] is represented and seeks summation operation；

S22：Threshold detection is carried out to all measurement units using initial threshold, the radar measurement sequence after being processedWherein z_nK () value is as follows：

In subsequent treatment, the measurement unit that value is all 0 is skipped；In this manner it is possible to the influence of exclusive segment noise, so as to drop The data volume of low computer disposal.

3. it is according to claim 1 it is a kind of based on distance matching PD radar weak target Dynamic Programming detection methods, its Be characterised by described in step 3 apart from matched processing method：

S31：Calculate each pulse recurrence frequency F_mFuzzy interval number Φ_m

Φ_m=Int (R_max/Ru_m),

Wherein Int () represents rounding operation, m=1,2 ..., M；

S32：At the k moment, F is passed through for radar_mThe n-th measurement z for measuring_n(k), by its distanceMatch l_mIt is individual fuzzy Interval, wherein l_m=1,2 ..., Φ_m, a matching distance can be obtained

S33：Add corresponding orientationEcho amplitude A_n(k), fuzzy interval valued numbers l_mEtc. information, in l_mIn individual fuzzy interval Build a distance matching and measure vector

S34：Make fuzzy interval valued numbers l_mIn l_m=1,2 ..., Φ_mIn continuous value, obtain one group of distance matching measurement sequence

S35：Order measures sequence number n in n=1,2 ..., N_kIn continuous value, the distance matching for obtaining all measurements of k moment measures sequence Row Z '_k：

S36：According to the definition of S34 and S35, Z '_kFor a line number is Φ_mN_kSequence, be simplified shown as：

$Z_k^{\′}\stackrel{\mathrm{\Δ}}{=}\left[\begin{array}{c}{z^{\′}}_1\left(k\right)\\ .\\ .\\ .\\ {z^{\′}}_i\left(k\right)\\ .\\ .\\ .\\ {z^{\′}}_{{\mathrm{\Φ}}_m{N}_k}\left(k\right)\end{array}\right],$

Wherein, z'_iK () represents Z '_kIn the matching of the i-th row distance measure vector, vector form and step S33Unanimously, i =1,2 ..., Φ_mN_k；

Due to having carried out equiprobability matching to all fuzzy intervals of radar apart from matching treatment process, therefore necessarily there is distance Fall in target realistic blur interval with measurement；It can be seen that, the measurement sequence Z ' after treatment_kIn certainly exist the substantial amount of target Survey, so as to recover the temporal correlation of metric data.