CN107656245A - A kind of method being applied to information fusion in radar signal sorting - Google Patents

A kind of method being applied to information fusion in radar signal sorting Download PDF

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CN107656245A
CN107656245A CN201710722269.4A CN201710722269A CN107656245A CN 107656245 A CN107656245 A CN 107656245A CN 201710722269 A CN201710722269 A CN 201710722269A CN 107656245 A CN107656245 A CN 107656245A
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CN107656245B (en
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郭立民
张艳苹
陈涛
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Harbin Engineering University
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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
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
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Abstract

The invention discloses a kind of method information fusion being applied in radar signal sorting, belong to radar signal processing field.The present invention carries out pixel-based fusion before radar signal sorting to pulse descriptive word (PDW), and feature-based fusion has been carried out to separation results after sorting.The Time Registration Method based on one-level pulse arrival time (TOA) difference matching is proposed in pixel-based fusion, pair pulse application D S evidence theories reached simultaneously sentence association to obtain new PDW；The parameter unification of same radar will be described in feature-based fusion and to separation results progress reliability order.Solve single receiving device and receive pulse missing sorting failure that may be present, and the problem of same portion's radar characterising parameter is incomplete same after sorting.Simulation result shows that the success rate of radar signal sorting can be effectively improved by pixel-based fusion, and the application of feature-based fusion, which can obtain, more succinctly intuitively radiates source information.

Description

A kind of method being applied to information fusion in radar signal sorting
Technical field
The invention belongs to radar signal processing field, and in particular to information fusion is applied in radar signal sorting by one kind Method.
Background technology
With the development of science and technology, the electromagnetic signal environment of modern battlefield is increasingly sophisticated, and radiation source density is multiplied, Electromagnetic signal pattern is also complicated and changeable all the more.Modern radar has multimode and disguise, and a radar often has a variety of works Operation mode realizes different purposes functions, and each pattern also has a corresponding technical characteristic parameter, and the thunder such as low probability of intercept Up to the utilization of interference protection measure, the Partial Feature parameter that scouting receiving device obtains missing is present, have imperfect, uncertain Characteristic, bring very big difficulty for radar signal sorting.In view of each scouting receiving device in certain section of observation time Information has complementarity, and the pulse descriptive word (PDW) that can be received by the method for information fusion to receiver merges Sorted again after processing, obtain separating effect more more preferable than single receiving device.And same scouting receiving device Continuous Observation sorting The result gone out should possess the characteristics of repeating, but be influenceed by factors such as deinterleaving algorithm and measurement accuracy, describe same portion's radar Parameter may be incomplete same, it is by the fusions of separation results that each parameter is unified and the radar to sub-electing carries out confidence level Sequence, succinctly can intuitively react the radiation source information in observation time.
At present, domestic and foreign scholars are mainly to being radiated after sorting for information fusion and the research direction of radar signal sorting Source carries out fusion recognition.Wu Zhen in 2015 is strong et al. probabilistic neural network, more meta-attribute blending algorithms and D-S evidence theory Effect ground combines, it is proposed that a kind of information fusion model on recognizing radar radiation source, the model is first with probabilistic neural net The radar parameter that network is obtained to radar reconnaissance equipment is identified, and when recognition result is not unique, then uses multivariate information fusion Algorithm is identified again for the possible radar model that neural network recognization goes out, and finally determines recognition result.Before sorting Limited in terms of pulse descriptive word fusion by document, can not find out related article, if but data before sorting are merged, it can retain More battle field informations, probability of detection and recognition capability to target can be effectively improved.
The content of the invention
It is an object of the invention to provide information fusion is applied in radar signal sorting, to realize than single equipment more Good separating effect, obtain a kind of more succinct intuitively radiation the of source information and information fusion is applied in radar signal sorting Method.
The purpose of the present invention is realized by following technical solution：
The present invention carries out pixel-based fusion first against the PDW that different receivers obtain, and next same receiver is connected The pulse data that continuous observation time obtains carries out the fusion of separation results, i.e., separation results is carried out with feature-based fusion, therefore believe Application of the breath fusion in radar signal sorting merges two parts comprising data level PDW fusions and feature level separation results.
A kind of 1. method being applied to information fusion in radar signal sorting, it is characterised in that melt including data level PDW Close and feature level separation results merge two parts, wherein：
Data level PDW fusions mainly include the following steps that：
(1) time alignment, the identical time will be registrated to from the unsynchronized data of each receiver；
(1.1) one-level TOA difference matchings are carried out to two groups of observation data of same period, by the way that observation data are calculated One-level TOA it is poor；
(1.2) by etoa1And etoa2Order composition l n dimensional vector ns X1(i)、X2(j), i.e., select what l pulse formed respectively Stream of pulses is matched
(1.3) X is counted1And X (i)2(j) number k and l dimension data of the corresponding error in measurement error between is corresponding Error and error；
(1.4) first order calculation TOA differences likelihood；
(1.5) it is best match position to set corresponding error minimum value (min { er (i, j) }), defines first group in error In the range of relative time for two groups of observation data relative time Δ t；
(2) Attribute Association, carrier frequency, pulsewidth support are calculated to the pulse reached in the same time after time alignment；
Carrier frequency f, pulsewidth pw diversity factor are mapped on the section of [0,1] by probability assignment function, calculate two ginsengs Several supports；
(3) overall support is sought using D-S fusion formulas, whether setting thresholding judges to reach pulse in the same time from same Radiation source；
D-S evidence theory represents interested proposition collection with identification framework U, defines the basic probability assignment function on U m:2U→ [0,1], meet：
M () is referred to as the Basic Probability As-signment on U, if m (A) ≠ 0, A is referred to as a burnt member, m (A) reactions to A can Reliability size；
For there is the inference system of two features, the probability assignment of two features is respectively m1、m2, for subset A, ask it Combine the overall Basic Probability As-signment under latter two feature collective effectD-S rule：
WhereinFor conflict item, if K ≠ 1, it is determined that m Basic Probability As-signment；If K=1, Then think m1With m2Contradiction, it is not necessary to combine.
(4) threshold judgement, a new data value is fused into the different pieces of information mutually in the same time from identical radiation source, and Data are arranged in new PDW by the sequencing of arrival time,
If signal comes from same radiation source, calculate its arithmetic average as fusion after this moment pulse carrier frequency, Pwm value, for not being the pulse reached simultaneously, we sort it according to the first later of arrival time, obtain more comprehensive Information；
Wherein, for the situation of pulse advance, arrival time value after definition fusion for known two arrival times compared with Small value, pwm value are the larger pulse back edge of numerical value and the difference of the arrival time newly defined；Situation about being included for pulse, it is fixed Pulse arrival time and pwm value after justice fusion are all the corresponding parameter of the pulse included；If without above-mentioned phenomenon, by arrival Time sequencing arranges.
The fusion of feature level separation results mainly includes the following steps that：
Carried out by calculating the support of tri- its carrier frequency, pulsewidth, PRI attributes, then using D-S evidence theory rule of combination Attribute fusion obtains overall support, and each characteristic parameter for describing same portion's radar is weighted to obtain unified parameter.
Calculate the carrier frequency f supports m of radarfWith pulsewidth support mpw；
PRI points are fixed and irregular two types：
A. separation results are the normal radar that PRI is fixed：
One separation results of one group of observation period are Ta, a separation results of another group of observation period are Tb, then The difference of the two separation results is Δ Tab=| Ta-Tb|, the PRI supports m of two separation resultsPRIFor：
TεFor the repetition period error of reconnaissance system；
B. separation results are staggered PRI radar：
So that two is irregular as an example, the skeleton cycle of one group of observed result is Ta, two irregular recurrence intervals are Ta1And Ta2, another group Separation results show skeleton cycle Tb, two irregular recurrence intervals are Tb1And Tb2；The now PRI supports m of separation resultsPRIFor：
Wherein, PRI difference DELTA T=| Ta-Tb|, Δ Tabi=| Tai-Tbi|, i=1,2；
Probability assignment function formula under n evidence collective effect is：
If overall support is more than the threshold value of setting, two separation results for judging to be compared describe same portion Radar, feature-based fusion is taken turns doing to the result obtained after all detecting data sortings in observation time, if description is all same One radar, then using the PRI variances inverse of separation results as weight coefficient, realize the unification to same portion's radar signature parameter；
In order to avoid due to sorting mistake caused by deinterleaving algorithm, the PRI parameters of such as sorting display are actual PRI times Number, we also need statistics to describe the umber of pulse and frequency number of this radar, separation results during realizing that parameter is unified The frequency of appearance is high and the umber of pulse that uses is more, it is believed that this separation results is with a high credibility, passes through the arteries and veins to describing different radars Rush number and carry out sort method, can intuitively obtain the radiation source information in observation period.
The beneficial effects of the present invention are：
The data that some receivers for observing same target receive, due to environment, noise and may be deposited The factor such as pulse missing influence, may result in sorting failure using the directly sorting of single receiving device reconnaissance data. The present invention carries out pixel-based fusion first against the PDW that different receivers obtain, and solves single receiving device reception pulse and loses Sorting failure problem that may be present after mistake；
Next the fusion of separation results is carried out to the pulse data that the same receiver Continuous Observation time obtains, i.e., to dividing Select result to carry out feature-based fusion, solve the problems, such as that the characterising parameter of same portion's radar after sorting is incomplete same.
Brief description of the drawings
Fig. 1 is PDW Fusion Models；
Fig. 2 is characterized a grade separation results Fusion Model；
Fig. 3 is PDW time alignment schematic diagrams；
Fig. 4 is fusion rule；
Fig. 5 is lossing signal separation results；
Fig. 6 is signal sorting result after fusion.
Embodiment
The embodiment of the present invention is described further below in conjunction with the accompanying drawings：
A kind of method information fusion being applied in radar signal sorting, is specifically divided into data level PDW fusions and feature The fusion of level separation results, model is as depicted in figs. 1 and 2.
Data level PDW fusions comprise the following steps：
Step 1：Time alignment.It is mutually solely due to being placed in reception of each receiver of diverse location to radar signal It is vertical to carry out, it is understood that there may be the different delays of communication network, therefore each receiver on call time and there may be the time difference.Melting , it is necessary in same temporal referencing system, nonsynchronous information will be registrated into phase from the conversion of the data of multiple receivers before closing The same fusion moment.Alignment principles are as shown in figure 3, specific method is as follows：
1. two groups of observation data of pair same period carry out one-level TOA difference matchings, by the way that observation data are calculated One-level TOA is poor.
etoa1(i)=toa1(i+1)-toa1(i), i=1,2 ... n1-1 (1)
etoa2(j)=toa2(j+1)-toa2(j), j=1,2 ... n2-1 (2)
N in formula1And n2Respectively observe the umber of pulse of data, n1- 1 and n2- 1 is respectively the one-level TOA that will be matched The umber of pulse of difference.
2. by etoa1And etoa2Order composition l n dimensional vector ns X1(i)、X2(j).The pulse of l pulse composition is selected respectively Stream is matched.
X1(i)=[etoa1(i),etoa1(i+1)…etoa1], (i+l-1) i=1,2 ... (n1-1)-l+1 (3)
X2(j)=[etoa2(j),etoa2(j+1)…etoa2], (j+l-1) j=1,2 ... (n2-1)-l+1 (4)
3. count X1And X (i)2(j) number k and l dimension data of the corresponding error in measurement error between corresponds to error And error.
Error (i, j)=| X1(i)-X2(j) |, i=1,2 ... n1-l；J=1,2 ... n2-l (5)
First order calculation TOA difference likelihoods：
4. being ranked up by the size of the number k in error, the position that taking makes k be maximum is best match position；If k Number for maximum is not 1, then relatively more corresponding error and error.Setting corresponds to error minimum value (min { er (i, j) }) Best match position, define the relative time Δ t that first group of relative time in error range is two groups of observation data.
Δ t=toa1(i)-toa2(j) (7)
I, j is at best match position.
Step 2：Attribute Association, carrier frequency, pulsewidth support are calculated to the pulse reached in the same time after time alignment.Pass through Carrier frequency f, pulsewidth pw diversity factor are mapped on the section of [0,1] by probability assignment function, calculate the support of two parameters.Such as One signal of fruit is received by two receivers simultaneously, for a certain property parameters, such as carrier frequency f, the measurement of one group of observation data It is worth for fa, another group of measured value is fb, then the difference of the two measured values is Δ fab=| fa-fb|.Now, the two measured values it Between similarity mfFor：
Wherein fεIt is the measurement tolerance of sensor, selects the measurement accuracy for 4 times.
Similarly obtain the pulsewidth support m of two measured valuespw：
Step 3：Overall support is sought using D-S fusion formulas, setting thresholding judges to reach whether pulse comes from the same time Same radiation source.Scout signal of the measurement for the echo signal basic parameter that receiving device provides depending on target emanation source radiation Pattern, because the factors such as ambient noise, multipath influence, it may result in the loss or deviation of signal measurement parameter.D-S evidences Theory is the popularization in Bayesian inference method, can preferably describe " not knowing " and " not knowing " type problem, therefore can The PDW parameters received with being detectd using DS evidence theories formula to different receivers carry out fusion treatment.
D-S evidence theory represents interested proposition collection with identification framework U, defines the basic probability assignment function on U m:2U→ [0,1], meet：
M () is referred to as the Basic Probability As-signment on U.If m (A) ≠ 0, A is referred to as a burnt member, m (A) reactions to A can Reliability size.
Method of Evidence Theory has oneself rational composition rule, abbreviation D-S rules.For there is the reasoning system of two features System, the probability assignment of two features is respectively m1、m2, for subset A, ask it to combine total under latter two feature collective effect Body Basic Probability As-signmentD-S rules such as formula (11) shown in.
WhereinFor conflict item.If K ≠ 1, it is determined that m Basic Probability As-signment；If K=1, Then think m1With m2Contradiction, it is not necessary to combine.
Step 4：Threshold judgement, a new data are fused into the different pieces of information mutually in the same time from identical radiation source Value, and data are arranged in new PDW by the sequencing of arrival time.
By the Time Registration Method based on one-level TOA difference matchings, asynchronous information is registrated to identical and merged by us At the moment, by the data association technique based on D-S evidence theory, the letter that different receivers receive in synchronization can be calculated Number the degree of association, by compared with thresholding 0.5, we can be determined that whether signal carrys out same radiation source.If signal is from same Radiation source, then calculate carrier frequency, pwm value of its arithmetic average as this moment pulse after fusion.For not being to reach simultaneously Pulse, we by its according to arrival time first later sequence, obtain more comprehensive information.Specific fusion rule is shown in Fig. 4 It is shown.
1) situation of pulse advance is directed to, the arrival time value after definition fusion is smaller for known two arrival times Value, pwm value are the larger pulse back edge of numerical value and the difference of the arrival time newly defined.
2) situation about being included for pulse, pulse arrival time and pwm value after definition fusion are all the pulse pair included The parameter answered.
If 3) arranged without above-mentioned phenomenon by arrival time order.
Feature level separation results fusion method is as follows：
, can be by calculating its carrier frequency, pulsewidth, PRI tri- for the separation results under same receiver difference observation period The support of individual attribute, attribute fusion is carried out to emitter characteristic parameter using based on the rule of combination of D-S evidence theory, obtained Overall support.
The carrier frequency of radar, the attributes similarity of pulsewidth are identical with formula (8), (9), and PRI points are fixed and irregular two types.
1) separation results are the normal radar that PRI is fixed：
One separation results of one group of observation period are Ta, a separation results of another group of observation period are Tb, then The difference of the two separation results is Δ Tab=| Ta-Tb|.The PRI similarities m of two separation resultsPRIFor：
TεFor the repetition period error of reconnaissance system.
2) separation results are staggered PRI radar：
So that two is irregular as an example, the skeleton cycle of one group of observed result is Ta, two irregular recurrence intervals are Ta1And Ta2, another group Separation results show skeleton cycle Tb, two irregular recurrence intervals are Tb1And Tb2.The now PRI similarities m of separation resultsPRIFor：
Wherein, PRI difference DELTA T=| Ta-Tb|, Δ Tabi=| Tai-Tbi|, i=1,2.
The D-S rules of formula (11) are that we obtain after sorting for the only two evidences i.e. formula of carrier frequency and pulsewidth New parameter PRI is arrived.Probability assignment function formula under n evidence collective effect is：
If overall support is more than the threshold value of setting, two separation results for judging to be compared describe same portion Radar.Feature-based fusion is taken turns doing to the result obtained after all detecting data sortings in observation time, if description is all same One radar, then using the PRI variances inverse of separation results as weight coefficient, realize the unification to same portion's radar signature parameter.
In order to avoid due to sorting mistake caused by deinterleaving algorithm, the PRI parameters of such as sorting display are actual PRI times Number, we also need statistics to describe the umber of pulse and frequency number of this radar during realizing that parameter is unified.Separation results The frequency of appearance is high and the umber of pulse that uses is more, it is believed that this separation results is with a high credibility, is similarly only sorted by less umber of pulse Obtained result or separation results only occurs once in multiple sorting, then it is assumed that the confidence level of separation results is relatively low.By right The umber of pulse for describing different radars carries out sort method, can intuitively obtain the radiation source information in observation period.
Embodiment：
Embodiment 1：Assuming that have two reconnaissance equipment receivers while work, each information mutual scouted receiving device and obtained Difference, the PDW received by pixel-based fusion algorithm to receiver carries out fusion treatment and forms new PDW, before and after fusion PDW sorted respectively with conventional SDIF methods.Emulation constructs the pulse descriptive word of two normal radar signal mixing, and it is joined Number is as shown in table 1 below.
The radar simulation parameter setting of table 1
Pulse miss rate be 50% under conditions of, deleted signal with merge after signal sorting Comparative result, such as the institute of table 2 Show.
Separation results before and after the pixel-based fusion of table 2
If from table 2 it can be seen that there is missing as shown in the first via in table and second road signal in reception signal, it is impossible to correct Sub-elect PRI parameters and be 400us and 500us radar, and more umber of pulses are obtained after pixel-based fusion, Neng Goucheng Work(sorts two radars.
Embodiment 2：Test signal intercepts and captures influence of the Loss Rate to signal sorting.Using the simulation parameter of table 1, difference is lost Normal radar signal under mistake rate and the signal after pixel-based fusion have carried out conventional sorting.To the single channel of pulse missing be present Signal and the result that is sorted after pixel-based fusion are as shown in Figure 5 and Figure 6.
From fig. 5, it can be seen that in pulse missing rate during 10%~45%, the two paths of signals that loss be present all can Successfully sub-elect two radars of setting；When pulse missing rate reaches 50%, the two paths of signals of loss can only be sub-elected correctly PRI parameters are 500us normal radar, it is impossible to correct to sub-elect the radar that PRI parameters are 400us.It is from fig. 6, it can be seen that logical After crossing pixel-based fusion, remain to successfully sort when pulse missing rate reaches 60%, demonstrate the calculation of radar signal pixel-based fusion Method sorts the validity of success rate to improving.
Embodiment 3：Test feature level separation results syncretizing effect.Emulation 5 sights continuous to a certain reconnaissance receiver first The PDW packets reported after survey are sorted respectively, and separation results are as shown in table 3.It is measured data that data are sorted in emulation, Deinterleaving algorithm still uses SDIF methods.
The lower separation results of more than 3 observation of table
Multiple separation results that table 3 is obtained carry out feature-based fusion, and obtained result is as shown in table 4.Though it can be seen that So in the case where time domain is 1 observation period, this thunder of PRI=1.9613ms described in 76 pulses that receiver detects The umber of pulse reached is only 22, in addition time domain be 5 observation period under receiver do not detect describe this radar Pulse signal, but pass through repeatedly observation sorting accumulation, we can exclude sorting failure possibility, determine this PRI= 1.9613ms normal radar be implicitly present in.
The feature-based fusion result of table 4
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area For art personnel, the present invention can have various modifications and variations.Within the spirit and principles of the invention, that is made any repaiies Change, equivalent substitution, improvement etc., should be included in the scope of the protection.

Claims (6)

  1. A kind of 1. method being applied to information fusion in radar signal sorting, it is characterised in that including data level PDW fusion and Feature level separation results merge two parts, wherein：
    Data level PDW fusions mainly include the following steps that：
    (1) time alignment, the identical time will be registrated to from the unsynchronized data of each receiver；
    (2) Attribute Association, carrier frequency, pulsewidth support are calculated to the pulse reached in the same time after time alignment；
    (3) overall support is sought using D-S fusion formulas, setting thresholding judges to reach whether pulse comes from same radiation in the same time Source；
    (4) threshold judgement, is fused into a new data value to the different pieces of information mutually in the same time from identical radiation source, and by number According to being arranged in new PDW by the sequencing of arrival time,
    The fusion of feature level separation results mainly includes the following steps that：
    Attribute is carried out by calculating the support of tri- its carrier frequency, pulsewidth, PRI attributes, then using D-S evidence theory rule of combination Fusion obtains overall support, and each characteristic parameter for describing same portion's radar is weighted to obtain unified parameter.
  2. A kind of 2. method being applied to information fusion in radar signal sorting according to claim 1, it is characterised in that Step (1) is specially in described data level PDW fusions：
    (1.1) one-level TOA difference matchings are carried out to two groups of observation data of same period, by observation data are calculated one Level TOA is poor：
    etoa1(i)=toa1(i+1)-toa1(i), i=1,2 ... n1-1
    etoa2(j)=toa2(j+1)-toa2(j), j=1,2 ... n2-1
    N in formula1And n2Respectively observe the umber of pulse of data, n1- 1 and n2- 1 is respectively the one-level TOA differences that will be matched Umber of pulse；
    (1.2) by etoa1And etoa2Order composition l n dimensional vector ns X1(i)、X2(j) stream of pulses of l pulse composition, i.e., is selected respectively Matched：
    X1(i)=[etoa1(i),etoa1(i+1)…etoa1], (i+l-1) i=1,2 ... (n1-1)-l+1
    X2(j)=[etoa2(j),etoa2(j+1)…etoa2], (j+l-1) j=1,2 ... (n2-1)-l+1
    (1.3) X is counted1And X (i)2(j) number k and l dimension data of the corresponding error in measurement error between corresponds to error And error：
    Error (i, j)=| X1(i)-X2(j) |, i=1,2 ... n1-l；J=1,2 ... n2-l
    (1.4) first order calculation TOA differences likelihood：
    <mrow> <mi>&amp;epsiv;</mi> <mo>=</mo> <mfrac> <mi>k</mi> <mi>l</mi> </mfrac> <mo>&amp;times;</mo> <mn>100</mn> <mi>%</mi> </mrow>
    It is ranked up by the size of the number k in error, the position that taking makes k be maximum is best match position；If k is maximum The number of value is not 1, then relatively more corresponding error and error；
    (1.5) it is best match position to set corresponding error minimum value (min { er (i, j) }), defines first group in error range Interior relative time is the relative time Δ t of two groups of observation data：
    Δ t=toa1(i)-toa2(j)
    Wherein, i, j are at best match position.
  3. A kind of 3. method being applied to information fusion in radar signal sorting according to claim 1, it is characterised in that Step (2) is specially in described data level PDW fusions：
    Carrier frequency f, pulsewidth pw diversity factor are mapped on the section of [0,1] by probability assignment function, two parameters of calculating Support；
    The carrier frequency f measured values of one group of observation data are fa, another group of measured value is fb, then the difference of the two measured values is Δ fab=| fa-fb|；
    Support m between the two carrier frequency f measured valuesfFor：
    <mrow> <msub> <mi>m</mi> <mi>f</mi> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&amp;Delta;f</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>f</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <mo>|</mo> <mrow> <msub> <mi>&amp;Delta;f</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>-</mo> <mn>2</mn> <msub> <mi>f</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> <mo>|</mo> </mrow> <msub> <mi>f</mi> <mi>&amp;epsiv;</mi> </msub> </mfrac> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>f</mi> <mi>&amp;epsiv;</mi> </msub> <mo>&amp;le;</mo> <msub> <mi>&amp;Delta;f</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&amp;le;</mo> <mn>2</mn> <msub> <mi>f</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&amp;Delta;f</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <mn>2</mn> <msub> <mi>f</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein fεIt is the measurement tolerance of sensor, selects the measurement accuracy for 4 times；
    Similarly obtain the pulsewidth support m of two measured valuespw：
    <mrow> <msub> <mi>m</mi> <mrow> <mi>p</mi> <mi>w</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&amp;Delta;pw</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>pw</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <mo>|</mo> <mrow> <msub> <mi>&amp;Delta;pw</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>-</mo> <mn>2</mn> <msub> <mi>pw</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> <mo>|</mo> </mrow> <mrow> <msub> <mi>pw</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mfrac> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>pw</mi> <mi>&amp;epsiv;</mi> </msub> <mo>&amp;le;</mo> <msub> <mi>&amp;Delta;pw</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&amp;le;</mo> <mn>2</mn> <msub> <mi>pw</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&amp;Delta;pw</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&amp;GreaterEqual;</mo> <mn>2</mn> <msub> <mi>pw</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>.</mo> </mrow>
  4. A kind of 4. method being applied to information fusion in radar signal sorting according to claim 1, it is characterised in that Step (3) is specially in described data level PDW fusions：
    D-S evidence theory represents interested proposition collection with identification framework U, defines the basic probability assignment function m on U:2U→ [0,1], meet：
    <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mi>m</mi> <mrow> <mo>(</mo> <mi>&amp;phi;</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mi>A</mi> <mo>&amp;Subset;</mo> <mi>U</mi> </mrow> </munder> <mi>m</mi> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> <mo>=</mo> <mn>1</mn> </mrow> </mtd> </mtr> </mtable> </mfenced>
    M () is referred to as the Basic Probability As-signment on U, if m (A) ≠ 0, A is referred to as a burnt member, m (A) reacts the confidence level to A Size；
    For there is the inference system of two features, the probability assignment of two features is respectively m1、m2, for subset A, ask its combination Overall Basic Probability As-signment under latter two feature collective effectD-S rule：
    <mrow> <mi>m</mi> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>&amp;cap;</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>=</mo> <mi>A</mi> </mrow> </munder> <msub> <mi>m</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mn>1</mn> <mo>-</mo> <mi>K</mi> </mrow> </mfrac> </mrow>
    WhereinFor conflict item, if K ≠ 1, it is determined that m Basic Probability As-signment；If K=1, then it is assumed that m1With m2Contradiction, it is not necessary to combine.
  5. A kind of 5. method being applied to information fusion in radar signal sorting according to claim 1, it is characterised in that Step (4) is specially in described data level PDW fusions：
    If signal comes from same radiation source, its arithmetic average is calculated as the carrier frequency of this moment pulse, pulsewidth after fusion Value, for not being the pulse reached simultaneously, we sort it according to the first later of arrival time, obtain more comprehensively believing Breath；
    Wherein,
    For the situation of pulse advance, the smaller value of arrival time value two arrival times for known to after definition fusion, pulsewidth It is worth for the larger pulse back edge of numerical value and the difference of the arrival time newly defined；
    Situation about being included for pulse, pulse arrival time and pwm value after definition fusion, which are all that the pulse included is corresponding, joins Number；
    If without above-mentioned phenomenon, arranged by arrival time order.
  6. 6. according to claim 1, a kind of method being applied to information fusion in radar signal sorting described in 3, its feature exists In the fusion of described feature level separation results is specially：
    Calculate the carrier frequency f supports m of radarfWith pulsewidth support mpw；
    PRI points are fixed and irregular two types：
    A. separation results are the normal radar that PRI is fixed：
    One separation results of one group of observation period are Ta, a separation results of another group of observation period are Tb, then this two The difference of individual separation results is Δ Tab=| Ta-Tb|, the PRI supports m of two separation resultsPRIFor：
    <mrow> <msub> <mi>m</mi> <mrow> <mi>P</mi> <mi>R</mi> <mi>I</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&lt;</mo> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <mo>|</mo> <mrow> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>-</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> <mo>|</mo> </mrow> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mfrac> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> <mo>&lt;</mo> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&lt;</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> </mrow> </msub> <mo>&gt;</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    TεFor the repetition period error of reconnaissance system；
    B. separation results are staggered PRI radar：
    So that two is irregular as an example, the skeleton cycle of one group of observed result is Ta, two irregular recurrence intervals are Ta1And Ta2, another component choosing As a result skeleton cycle T is shownb, two irregular recurrence intervals are Tb1And Tb2；The now PRI supports m of separation resultsPRIFor：
    <mrow> <msub> <mi>m</mi> <mrow> <mi>P</mi> <mi>R</mi> <mi>I</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>&amp;Delta;</mi> <mi>T</mi> <mo>&amp;le;</mo> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> <msub> <mi>or&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> <mi>i</mi> </mrow> </msub> <mo>&amp;le;</mo> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <mo>|</mo> <mrow> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> <mi>i</mi> </mrow> </msub> <mo>-</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> <mo>|</mo> </mrow> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mfrac> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> <mo>&lt;</mo> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> <mi>i</mi> </mrow> </msub> <mo>&amp;le;</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mrow> <mo>|</mo> <mrow> <mi>&amp;Delta;</mi> <mi>T</mi> <mo>-</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> <mo>|</mo> </mrow> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mfrac> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> <mo>&lt;</mo> <mi>&amp;Delta;</mi> <mi>T</mi> <mo>&amp;le;</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> <mo>,</mo> <msub> <mi>&amp;Delta;T</mi> <mrow> <mi>a</mi> <mi>b</mi> <mi>i</mi> </mrow> </msub> <mo>&gt;</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mi>&amp;Delta;</mi> <mi>T</mi> <mo>&gt;</mo> <mn>2</mn> <msub> <mi>T</mi> <mi>&amp;epsiv;</mi> </msub> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>
    Wherein, PRI difference DELTA T=| Ta-Tb|, Δ Tabi=| Tai-Tbi|, i=1,2；
    Probability assignment function formula under n evidence collective effect is：
    <mrow> <mi>m</mi> <mrow> <mo>(</mo> <mi>A</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>0</mn> <mo>,</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <mfrac> <mrow> <munder> <mo>&amp;Sigma;</mo> <mrow> <mo>&amp;cap;</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>A</mi> </mrow> </munder> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>m</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> <mrow> <mn>1</mn> <mo>-</mo> <munder> <mo>&amp;Sigma;</mo> <mrow> <mo>&amp;cap;</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>=</mo> <mi>&amp;phi;</mi> </mrow> </munder> <munderover> <mo>&amp;Pi;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>n</mi> </munderover> <msub> <mi>m</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>A</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow> </mfrac> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> <mo>&amp;ForAll;</mo> <mi>A</mi> <mo>&amp;Subset;</mo> <mi>U</mi> <mo>,</mo> <mi>A</mi> <mo>&amp;NotEqual;</mo> <mi>&amp;phi;</mi> </mrow>
    If overall support is more than the threshold value of setting, two separation results for judging to be compared describe same portion's thunder Reach, feature-based fusion is taken turns doing to the result obtained after all detecting data sortings in observation time, if description is all same Portion's radar, then using the PRI variances inverse of separation results as weight coefficient, realize the unification to same portion's radar signature parameter；
    In order to avoid due to sorting mistake caused by deinterleaving algorithm, the PRI parameters of such as sorting display are actual PRI multiple, I During realizing that parameter is unified, also need statistics to describe the umber of pulse and frequency number of this radar, separation results occur Frequency it is high and the umber of pulse that uses is more, it is believed that this separation results is with a high credibility, passes through the umber of pulse to describing different radars Sort method is carried out, can intuitively obtain the radiation source information in observation period.
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