CN109116323A - The optimization method and equipment of IFF system false-alarm thresholding based on polynomial curve fitting - Google Patents
The optimization method and equipment of IFF system false-alarm thresholding based on polynomial curve fitting 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
- 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
Abstract
The optimization method and equipment of the invention discloses a kind of IFF system false-alarm thresholding based on polynomial curve fitting, this method comprises: having identified the automatic admission and storage of target data in the power range of airspace；Data prediction；Construct multinomial model coefficient matrix；Fitting of a polynomial solves；Difference fitting number of iterations and error analysis；False-alarm threshold curve generates；False-alarm threshold curve imports sector and integration；Effect adjustment and comprehensive assessment.Optimization method and equipment of the invention can inhibit the false target generated by position environmental factor effectively in real time, false-alarm probability parameter value need to be only arranged in overall process, without manually calculating, optimization efficiency is high, meet the war fighting requirement of " quickly identification, accurate positionin ", while greatly reducing cost of human and material resources.
Description
Technical field
The present invention relates to west system enemy and we to identify data processing field, more particularly to a kind of based on polynomial curve fitting
IFF system false-alarm thresholding optimization method and equipment.
Background technique
Ⅻ A IFF of Mark (Identification Friend or Foe) system is the enemy of west system latest generation
One of the important means of my identifying system and Modern Information battlefield military confrontation.Identification of friend or foe " precise positioning,
Quickly identification " function, greatly strengthens the harmony between the accuracy of operational commanding and each combat unit, accelerates system significantly
Reaction speed reduces and accidentally injures probability, uses particularly suitable for more arm of the services combined operation.In the operational environment of actual complex, IFF
Identification of friend or foe is influenced by geographical environment, thunder and lightning weather and artificial malice electromagnetic interference, leads to enemy and we's determined property
The probability of mistake greatly increases, and mistake beats accidental injury event and emerges one after another.The efficient identification ability for how improving identification of friend or foe, is picked
Except false jamming target, it appears particularly important.
In air defence identification region, IFF identification of friend or foe follows radar system or weapon platform rack-mounted according to military requirement
In air defense position, in order to assess the fighting efficiency with test macro entirety, position field optimizing need to be carried out.In position field optimizing
In the process, comprehensively consider the factor for influencing target identification performance, mainly include geographical location, electromagnetic environment, gas locating for position
As environmental factors and artificial malicious interference factors such as, barriers.According to identification of friend or foe it is practical war technical indicator requirement,
The indices parameter etc. of safeguards system operational performance is optimized, eliminates atural object, meteorology and electromagnetic interference etc. to greatest extent
Bring influences, and raising IFF enemy and we identify the performance equipped as far as possible, realizes the best match of equipment, position environment, sends out to the greatest extent
The maximum fighting efficiency for shooting radar, provides reliable basis for intelligence & command and strategic decision.
It is identified in equipment position field optimizing in traditional enemy and we, the generation of false-alarm thresholding and optimizes and revises dependence and have experience
Engineer.Firstly, the generation of false-alarm thresholding, which relies on engineer, counts a large amount of target datas (including IFF system in identification region
The short distance in power range of uniting and remote response identify data)；It is generated after carrying out data analysis by manual type again
Original false-alarm thresholding is imported into progress effect debugging in decoding software, by observing and counting false-alarm by original false-alarm thresholding
Thresholding is unsatisfactory for practical war fighting requirement if false target is more to the inhibitory effect of false target, starts again point
The raw original false-alarm thresholding of division and verification process.This manual type subjectivity is strong, inefficiency, it is not flexible, can not be to decoding
Effect carries out effective advance evaluation, and the validity and reasonability of original false-alarm thresholding can only be by observing and statisticalling analyze reality
The reply data of a large amount of targets in border is to verify, and largely optimization operating procedure is complicated and time-consuming, and the effect of false-alarm thresholding is excellent
Change process needs to adjust repeatedly.Even if with model radar due to set up position difference, by different complicated geographical environments and electricity
The interference of magnetic environment factor, false-alarm thresholding needs are regenerated and are adjusted, to get a desired effect, from the generation of false-alarm thresholding
It often needs to take a substantial amount of time with effect adjustment, man power and material, and optimization efficiency is low.If in the war preparedness phase, Jiu Huiyan
Ghost image is thundering up to the requirement of real-time operational exertion, bungles the chance of winning a battle.Even if with model radar because setting up position difference, the production of false-alarm thresholding
Raw and optimizing and revising for effect all needs experienced engineer again and again to debug again, and optimization efficiency is low.
Summary of the invention
The technical problems to be solved by the present invention are: in view of the above-mentioned problems existing in the prior art, the invention proposes one
The optimization method and equipment of IFF system false-alarm thresholding of the kind based on polynomial curve fitting, can inhibit effectively in real time because of position
False-alarm probability parameter value need to be only arranged in the false target that environmental factor generates, overall process, calculate without artificial, and optimization efficiency is high,
Meet the war fighting requirement of " quickly identification, accurate positionin ", while greatly reducing cost of human and material resources.
A kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting provided by the invention, comprising:
Step 1: target response data have been identified in admission IFF system covering power range in real time, according to every group of answer number
According to characterisitic parameter counted, and according to every group of response target different direction, different distance carry out classification storage；
Step 2: the target response data of identification of storage being pre-processed, are rejected in target response data in boundary point
The data of amplitude variation abnormality；
Step 3: according to the data prediction of step 2 as a result, according to multinomial model, constructing multinomial model coefficient matrix
A becomes sequency spectrum or row non-singular matrix；
Step 4: generalized inverse matrix A is found out to the matrix A for meeting step 3+, and carry out fitting of a polynomial solution；
Step 5: according to the difference between fitting and smoothing effect and match value and observation, determining that the best multinomial of effect changes
Generation number k；
Step 6: generating false-alarm threshold curve；
Step 7: sector is carried out to different sectors, that is, different direction, the false-alarm threshold data of different distance and imports and integrates,
Form the false-alarm threshold data in IFF system covering power range；
Step 8: under the conditions of default false-alarm probability, according to the requirement for having identified target point track information in IFF system,
The inhibitory effect of comprehensive assessment false-alarm thresholding, the final up-regulation range parameter Δ and the number of iterations k for determining false-alarm threshold curve.
Further, the step 2 specifically includes:
Step 21: utilize " mean value " screen principle, to stored different direction, the target response data of different distance into
Row statistics, and mark the data in boundary point amplitude variation abnormality；
Step 22: abnormal data marked in step 21 being rejected, and the data after rejecting are deposited again
Storage processing.
Further, the step 3 specifically includes:
Step 31: according to Least Square Theory and best approximation theory, establishing polynomial curve fitting model, utilization is multinomial
Formula model y=f (x)=β0+β0x+...+βnxnTo be fitted m data (xi,yi), wherein the vector error factor isN is fitting of a polynomial order, solves formula y=f (x)=β0+β0x+...+
βnxnOptimum fit curve be equivalent to solving equationsOptimal approximation solution,
In enable A ∈ cm×(n+1)It is the coefficient matrix of above-mentioned equation group left end, y is the vector of above-mentioned equation group right end, and above-mentioned equation group
Solution vector be β=(β0,β1,...,βn)T；
Step 32: the multinomial model established according to step 31, to the pretreated target response data of step 2, according to
Matrix theory eliminates the data of the proportional relationship of ranks in data, and construction multinomial model coefficient matrices A becomes sequency spectrum or row
Non-singular matrix.
Further, the step 4 specifically includes:
Step 41: according to matrix theory, if A is row non-singular matrix, A+=AH(AAH)-1；If A is sequency spectrum square
Battle array, then A+=(AAH)-1AH；
Step 42: according to the solution formula of above-mentioned equation group, calculating solution β=(β0,β1,...,βn)T=A+y。
Further, the step 5 specifically includes:
Step 51: utilizing the vector error factorCalculate different direction, the target of different distance
Error between response truthful data and fitting data generates error vector, fitting effect when comparative analysis difference the number of iterations k
Fruit；
Step 52: smooth effect and error vector when according to the different the number of iterations of selection comprehensively consider the number of iterations, put down
Sliding effect, vector error and arithmetic speed and time, determine the best fitting of a polynomial number k of effect.
Further, the step 6 specifically includes:
Step 61: the polynomial iterative number k determined according to step 5 calculates optimal approximation solution；
Step 62: the false-alarm thresholding for generating characterization response target " amplitude-distance " relationship in IFF system power range is bent
Line.
Further, the step 7 specifically includes:
Step 71: by the 360 ° of airspace coverings of IFF system, being divided into 12 sectors, 30 ° of each sector；The distance of each sector
From 0 to power range maximum value principle divide, by different direction locating for fit object, the false-alarm threshold data of distance, lead
Enter to corresponding sector；
Step 72: integrate false-alarm thresholding, i.e., the false-alarm threshold data imported to different direction, different distance is integrated,
Form the false-alarm threshold data in IFF system covering power range.
Further, the step 8 specifically includes:
Step 81: aobvious according to the point track for having identified target in IFF system under conditions of setting false-alarm probability 0.1%
Show effect, carry out the adjustment of inhibitory effect, if false alarm rate height or point mark continuity and flocculating result are poor, comparison provides thresholding song
Line overall magnitude raises range parameter Δ and the number of iterations k；
Step 82: in conjunction with false-alarm probability, point track continuity, flocculating result and inhibitory effect, comprehensive assessment false-alarm thresholding
The inhibitory effect of curve, the up-regulation range parameter Δ and the number of iterations k of the final false-alarm threshold curve for determining meet demand.
Another aspect of the present invention provides a kind of optimization equipment of IFF system false-alarm thresholding based on polynomial curve fitting,
Include:
Data recording and storage device have identified target response number for being enrolled in IFF system covering power range in real time
According to, counted according to the characterisitic parameter of every group of reply data, and according to every group of response target different direction, different distance into
Row classification storage；
Data prediction device rejects target response for pre-processing to the target response data of identification of storage
In the data of boundary point amplitude variation abnormality in data；
Multinomial model coefficient matrix constructing apparatus, for the data prediction according to data prediction device as a result, root
According to multinomial model, constructing multinomial model coefficient matrices A becomes sequency spectrum or row non-singular matrix；
Fitting of a polynomial solving device, for being found out extensively to the matrix A for meeting multinomial model coefficient matrix constructing apparatus
Adopted inverse matrix A+, and carry out fitting of a polynomial solution；
The number of iterations determining device, for determining effect according to the difference between fitting and smoothing effect and match value and observation
The best polynomial iterative number k of fruit；
False-alarm threshold curve generating means, for generating false-alarm threshold curve；
False-alarm thresholding imports and integrating apparatus, for the false-alarm threshold number to different sectors, that is, different direction, different distance
It imports and integrates according to sector is carried out, form the false-alarm threshold data in IFF system covering power range；
Effect adjustment and comprehensive evaluating device, are used under the conditions of default false-alarm probability, according to the identification in IFF system
The requirement of target point track information, the inhibitory effect of comprehensive assessment false-alarm thresholding, the final upper amplitude modulation for determining false-alarm threshold curve
Spend parameter, Δ and the number of iterations k.
A kind of computer readable storage medium that another aspect of the present invention provides, is stored thereon with computer program, special
The step of sign is, the computer program realizes method as described above when being executed by processor.
Beneficial effects of the present invention:
1, in the field optimizing of tradition IFF identification of friend or foe position, the generation of false-alarm thresholding is manually united entirely
Count, handle the mode of a large amount of target response data.It is proposed by the present invention to apply polynomial fitting, false-alarm door can be automatically generated
It limits, and is automatically adjusted the processing method of effect according to preset alarmed falsely probability.The application of this method solves traditional artificial
The cumbersome procedure for counting reply data and analyzing and processing data, substantially increases position optimizing efficiency, improves IFF enemy and we's identification
The working performance of system；
2, in traditional IFF identification of friend or foe position field optimizing, the effect debugging problem of false-alarm thresholding.False-alarm
The effect debugging of the inhibition false target of thresholding needs experienced engineer to garrison position observation and adjustment effect for a long time, leads to
It crosses in continuous comparative analysis statistics radar power bounds, especially short distance, remote target response data, analysis is empty
Alert thresholding is to the inhibitory effect of false target, if effect bad the phenomenon that need to continuing to optimize.It is proposed by the present invention by software side
Formula is dissolved into position optimizing system, and software processes are performed fully automatic, and can automatically generate false-alarm door using algorithm in software
Limit, and the effect Automatic Optimal of false-alarm thresholding can be carried out according to preset false-alarm probability parameter, overall process is not necessarily to manual intervention, greatly
The cost for adjusting effect is reduced greatly, improves working efficiency；
3, in traditional IFF identification of friend or foe position field optimizing, cause every thunder due to setting up position difference
Different up to required false-alarm threshold parameter, even if setting up with model radar, position is different, the generation of false-alarm thresholding and effect it is excellent
Changing adjustment all needs experienced engineer again and again to debug again, the low phenomenon of optimization efficiency.Target proposed by the present invention
False-alarm thresholding automatically generate and effect optimization in adjust automatically method and application software mode realize the complete of data processing
Automated manner.Which is solved and the false-alarm thresholding to every radar need to be carried out manually again since radar sets up position difference
The cumbersome debugging process for being arranged and adjusting, substantially increases the efficiency of optimization, reduces manpower, object in radar erection process
Power, financial resources cost.This method have the characteristics that it is flexible and efficient, optimize and revise it is convenient, fast, and can real-time inspection effect of optimization；
4, the full automatic treatment of the generation of IFF identification of friend or foe false-alarm thresholding proposed by the present invention and effect optimization debugging
Method, the roadmap and processing mode of this method can also use for reference the angle measurement of pulse IFF identification of friend or foe, be used for system
OBA (Off-Boresight Angle) table automatically generating and correcting, and monopulse secondary surveillance radar system angle measurement essence can be improved
Degree and efficiency.
5, proposed by the present invention to handle IFF identification of friend or foe false-alarm thresholding using multinomial model and best approximation theory
Method, the roadmap can also be used for Industry Control, data statistic analysis subject in reader, reverse-engineering, spectrum analysis
Etc. related fieldss, specifically combine actual demand, fitting of a polynomial model can also with Fourier space model, power series model or its
Its mathematical combination formula model is substituted.
Detailed description of the invention
Examples of the present invention will be described by way of reference to the accompanying drawings, in which:
Fig. 1 is the flow diagram of the embodiment of the present invention；
Fig. 2 is that the false-alarm thresholding of the embodiment of the present invention inhibits schematic illustration；
Fig. 3 is the algorithm flow schematic diagram of the solution optimal approximation solution of the embodiment of the present invention；
Fig. 4 is application schematic diagram of the optimization method of the embodiment of the present invention in radar site optimization processing link.
Specific embodiment
All features disclosed in this specification or disclosed all methods or in the process the step of, in addition to mutually exclusive
Feature and/or step other than, can combine in any way.
Any feature disclosed in this specification unless specifically stated can be equivalent or with similar purpose by other
Alternative features are replaced.That is, unless specifically stated, each feature is an example in a series of equivalent or similar characteristics
?.
The stream of the optimization method of the IFF system false-alarm thresholding based on polynomial curve fitting of the position Fig. 1 embodiment of the present invention
Journey schematic diagram, specific steps are as follows:
Step 101: in the power range of airspace, having identified the automatic admission and storage of target data.It is identified according to IFF enemy and we
System false-alarm threshold generation in position optimizing software instructs, in airspace identification power range, after receiver IF process
Identify that the real-time reply data of target carries out automatic admission and handles with storage；Target response data have been identified in real-time admission
Meanwhile it being counted according to the characterisitic parameter of every group of reply data (amplitude, distance, azimuth information), and according to every group of response mesh
It is marked on different direction (sector), different distance section carries out classification storage.
Step 102: data prediction.It is right on the constringent influence in fitting algorithm boundary individually " exceptional value " in order to reduce
The target response data of identification of automatic admission and classification storage are pre-processed, that is, to " abnormal in target response data
Value ", i.e., rejected in the data of boundary point amplitude variation abnormality.
Step 103: construction multinomial model coefficient matrix.According to the data prediction of step 102 as a result, according to multinomial
Model, structural matrix A become sequency spectrum or row non-singular matrix.
Step 104: fitting of a polynomial solves.Generalized inverse matrix A is found out to the matrix for meeting step 102+, and then find out most
Good Approximating Solutions.
Step 105: difference fitting number of iterations and error analysis.According between fitting and smoothing effect and match value and observation
Difference, determine the best the number of iterations n of effect.
Step 106: false-alarm threshold curve generates.Threshold curve is generated according to the optimal approximation solution found out.
Step 107: false-alarm threshold curve imports sector and integration.With software mode to different sectors, different distance section
False-alarm threshold data carries out sector and imports and integrate, and forms the false-alarm threshold number in IFF identification of friend or foe covering power range
According to.
Step 108: effect optimization and the comprehensive assessment of IFF system performance.Under the conditions of default false-alarm probability, according to IFF system
The requirement of target point track information in system position optimizing, the inhibitory effect of comprehensive assessment false-alarm thresholding are final to determine false-alarm thresholding
The up-regulation range parameter Δ and the number of iterations n of curve.
Fig. 2 is the schematic illustration of the Data processing application false-alarm threshold curve of step 106,107 and 108.The schematic diagram
Illustrate the Contrast on effect to received target response data in IFF system power range, before and after using false-alarm thresholding,
Using strong noise jamming signal is effectively inhibited after false-alarm thresholding, it is extracted the principle of effective target signal, while also characterizing
The amplitude (ordinate) of false-alarm threshold curve is with the function changing relation between distance (abscissa).Step 106,107 and 108 points
Not the following steps are included:
Step 1061: the polynomial iterative frequency n determined according to step 105 calculates optimal approximation solution；
Step 1062: according to the inhibiting effect principle of false-alarm thresholding, IFF system power model being gone out by position optimizing Software on Drawing
The false-alarm threshold curve of characterization response target " amplitude-distance " relationship in enclosing.
Step 1071: importing corresponding matched curve data by different sectors (orientation).It is covered by 360 ° of airspaces of IFF system
Lid, is divided into 12 sectors, 30 ° of each sector；The distance of each sector from 0 to power range maximum value (500km) principle draw
Point.Corresponding sector is imported by different distance locating for fit object, the matched curve data in orientation by software mode；
Step 1072: integrating false-alarm thresholding, i.e., the false-alarm threshold data imported to different direction, different distance carries out whole
It closes, forms the false-alarm threshold data in IFF system covering power range.
Step 1081: inhibitory effect adjustment.It is whole according to IFF identification of friend or foe under conditions of setting false-alarm probability 0.1%
The point Target track displaying effect for having identified target in display and control software is held, the adjustment of inhibitory effect is carried out.If false alarm rate is high or puts mark
Continuity and flocculating result are poor, and false target cannot be inhibited very well by illustrating that suppression threshold is lower, then it is whole to provide threshold curve for comparison
Body amplitude raises parameter, Δ and the number of iterations n；
Step 1082: comprehensive assessment.It is commented in conjunction with false-alarm probability, point track continuity, flocculating result and inhibitory effect, synthesis
Estimate the inhibitory effect of false-alarm threshold curve, it is final to determine the up-regulation range parameter for meeting the false-alarm threshold curve of radar site optimization
Δ and the number of iterations n.
Fig. 3 is step 102,103,104,105,106 Data processing application multinomial models, and matrix theory is to false-alarm door
Limit the flow diagram of curved line arithmetic solution, fitting, error evaluation processing.Step 102,103,104,105 and 106 are specific respectively
The following steps are included:
Step 1021: utilizing " mean value " to screen principle, to stored different sectors orientation, the target data of different distance
It is counted, and marks the data in boundary point amplitude variation abnormality.
Step 1022: rejecting " exceptional value ", abnormal data marked in step 1021 is rejected, especially IFF
In identification of friend or foe power range, the abnormal data of boundary is rejected, and is stored again to the data after rejecting
Processing.
Step 1031: according to least square and best approximation theory, establishing polynomial curve fitting model.Multinomial model
For y=f (x)=β0+β0x+...+βnxn(1) it is fitted m data (xi,yi) (i=1,2 ... m), wherein the vector error factor
ForN is fitting of a polynomial order.Most for solution (1) formula
Good matched curve is equivalent to solving equationsOptimal approximation solution, wherein enabling A
∈cm×(n+1)It is the coefficient matrix of formula (3) left end, y is the vector of formula (3) right end.And the solution vector of formula (3) is β=(β0,
β1,...,βn)T。
Step 1032: construction multinomial model coefficient matrices A.According to the multinomial model that step 1031 is established, according to step
To pretreated target data in rapid 102, the data of the proportional relationship of ranks in data, construction are eliminated according to matrix theory
(3) matrix A becomes sequency spectrum or row non-singular matrix.
Step 1041: solving generalized inverse matrix A+.According to matrix theory, if A is row non-singular matrix, A+=AH(AAH
)-1；If A is sequency spectrum matrix, A+=(AAH)-1AH.In actual operation, as A ∈ Rm×nWhen, then AH=AT, known in IFF
Other target data belongs to real number field, therefore available ATInstead of AHIt is calculated；
Step 1042: fitting of a polynomial solves.According to the solution formula of (3) formula equation group, solution β=(β is calculated0,
β1,...,βn)T=A+y。
Step 1051: fitting effect when comparative analysis difference the number of iterations n.Utilize the meter of (2) formula vector error factor
Different distance is calculated, the error between the target response truthful data and fitting data of different direction generates error vector；
Step 1052: determining the number of iterations n of multinomial model.According to select different the number of iterations when, smooth effect and
Error vector comprehensively considers the number of iterations, smooth effect, vector error, the arithmetic speed of radar terminal software and time, determination
The optimal fitting of a polynomial frequency n of effect out.
Step 1061: the polynomial iterative frequency n determined according to step 105 calculates optimal approximation solution；
Step 1062: false-alarm threshold curve generates.Characterization in IFF system power range is gone out by position optimizing Software on Drawing
The false-alarm threshold curve of response target " amplitude-distance " relationship.
Fig. 4 is that the optimization method of the IFF identification of friend or foe false-alarm thresholding based on polynomial curve fitting and IFF enemy and we know
Relation schematic diagram between other system position optimizing, the processing of IFF identification of friend or foe position optimizing includes: answering machine mark in Fig. 4
School, receiver parameters optimization, decoding parameter optimization etc., work of the invention is excellent just for the position of IFF identification of friend or foe
In change false-alarm thresholding curve fitting algorithm carry out theory and practice application research further include in actual position optimizing
It draws position masking figure, transmitting, reception optimization, the optimization of point mark, System Performance Analysis optimization etc. and is not belonging to emphasis of the invention,
It is not repeated herein.
Another aspect of the present invention also proposed one kind and optimize equipment correspondingly with the step of above-mentioned optimization method, wrap
It includes:
Data recording and storage device have identified target response number for being enrolled in IFF system covering power range in real time
According to, counted according to the characterisitic parameter of every group of reply data, and according to every group of response target different direction, different distance into
Row classification storage；
Data prediction device rejects target response for pre-processing to the target response data of identification of storage
In the data of boundary point amplitude variation abnormality in data；
Multinomial model coefficient matrix constructing apparatus, for the data prediction according to data prediction device as a result, root
According to multinomial model, constructing multinomial model coefficient matrices A becomes sequency spectrum or row non-singular matrix；
Fitting of a polynomial solving device, for being found out extensively to the matrix A for meeting multinomial model coefficient matrix constructing apparatus
Adopted inverse matrix A+, and carry out fitting of a polynomial solution；
The number of iterations determining device, for determining effect according to the difference between fitting and smoothing effect and match value and observation
The best polynomial iterative number k of fruit；
False-alarm threshold curve generating means, for generating false-alarm threshold curve；
False-alarm thresholding imports and integrating apparatus, for the false-alarm threshold number to different sectors, that is, different direction, different distance
It imports and integrates according to sector is carried out, form the false-alarm threshold data in IFF system covering power range；
Effect adjustment and comprehensive evaluating device, are used under the conditions of default false-alarm probability, according to the identification in IFF system
The requirement of target point track information, the inhibitory effect of comprehensive assessment false-alarm thresholding, the final upper amplitude modulation for determining false-alarm threshold curve
Spend parameter, Δ and the number of iterations k.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
To be done through the relevant hardware of the program instructions, which be can be stored in a computer readable storage medium, and storage is situated between
Matter may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access
Memory), disk or CD etc..
The invention is not limited to specific embodiments above-mentioned.The present invention, which expands to, any in the present specification to be disclosed
New feature or any new combination, and disclose any new method or process the step of or any new combination.
Claims (10)
1. a kind of optimization method of the IFF system false-alarm thresholding based on polynomial curve fitting characterized by comprising
Step 1: target response data have been identified in admission IFF system covering power range in real time, according to every group of reply data
Characterisitic parameter is counted, and carries out classification storage in different direction, different distance according to every group of response target；
Step 2: the target response data of identification of storage being pre-processed, are rejected in target response data in boundary point amplitude
The data of variation abnormality；
Step 3: according to the data prediction of step 2 as a result, according to multinomial model, construction multinomial model coefficient matrices A at
For sequency spectrum or row non-singular matrix；
Step 4: generalized inverse matrix A is found out to the matrix A for meeting step 3+, and carry out fitting of a polynomial solution；
Step 5: according to the difference between fitting and smoothing effect and match value and observation, determining the best polynomial iterative of effect
Number k；
Step 6: generating false-alarm threshold curve；
Step 7: sector being carried out to different sectors, that is, different direction, the false-alarm threshold data of different distance and imports and integrates, is formed
IFF system covers the false-alarm threshold data in power range；
Step 8: comprehensive according to the requirement for having identified target point track information in IFF system under the conditions of default false-alarm probability
Assess the inhibitory effect of false-alarm thresholding, the final up-regulation range parameter Δ and the number of iterations k for determining false-alarm threshold curve.
2. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 1,
It is characterized in that, the step 2 specifically includes:
Step 21: utilizing " mean value " to screen principle, unite to stored different direction, the target response data of different distance
Meter, and mark the data in boundary point amplitude variation abnormality；
Step 22: abnormal data marked in step 21 being rejected, and the data after rejecting are carried out at storage again
Reason.
3. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 1,
It is characterized in that, the step 3 specifically includes:
Step 31: according to Least Square Theory and best approximation theory, establishing polynomial curve fitting model, utilize polynomial module
Type y=f (x)=β0+β0x+...+βnxnTo be fitted m data (xi,yi), wherein the vector error factor isI=1,2 ... m, n are fitting of a polynomial order, solve formula y=f (x)=β0+β0x+...+βnxn
Optimum fit curve be equivalent to solving equationsOptimal approximation solution, wherein enabling A
∈cm×(n+1)The coefficient matrix of above-mentioned equation group left end, y is the vector of above-mentioned equation group right end, and above-mentioned solution of equations to
Amount is β=(β0,β1,...,βn)T；
Step 32: the multinomial model established according to step 31, to the pretreated target response data of step 2, according to matrix
Theory eliminates the data of the proportional relationship of ranks in data, and construction multinomial model coefficient matrices A becomes sequency spectrum or row full rank
Matrix.
4. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 1,
It is characterized in that, the step 4 specifically includes:
Step 41: according to matrix theory, if A is row non-singular matrix, A+=AH(AAH)-1；If A is sequency spectrum matrix, A+=(AAH)-1AH；
Step 42: according to the solution formula of above-mentioned equation group, calculating solution β=(β0,β1,...,βn)T=A+y。
5. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 3,
It is characterized in that, the step 5 specifically includes:
Step 51: utilizing the vector error factorCalculate the target response of different direction, different distance
Error between truthful data and fitting data generates error vector, fitting effect when comparative analysis difference the number of iterations k；
Step 52: smooth effect and error vector when according to the different the number of iterations of selection comprehensively consider the number of iterations, smooth effect
Fruit, vector error and arithmetic speed and time determine the best fitting of a polynomial number k of effect.
6. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 1,
It is characterized in that, the step 6 specifically includes:
Step 61: the polynomial iterative number k determined according to step 5 calculates optimal approximation solution；
Step 62: generating the false-alarm threshold curve of characterization response target " amplitude-distance " relationship in IFF system power range.
7. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 1,
It is characterized in that, the step 7 specifically includes:
Step 71: by the 360 ° of airspace coverings of IFF system, being divided into 12 sectors, 30 ° of each sector；The distance of each sector from 0 to
The principle of power range maximum value divides, and by different direction locating for fit object, the false-alarm threshold data of distance, imported into phase
The sector answered；
Step 72: integrating false-alarm thresholding, i.e., the false-alarm threshold data imported to different direction, different distance is integrated, formed
IFF system covers the false-alarm threshold data in power range.
8. a kind of optimization method of IFF system false-alarm thresholding based on polynomial curve fitting according to claim 1,
It is characterized in that, the step 8 specifically includes:
Step 81: under conditions of setting false-alarm probability 0.1%, being imitated according to the point Target track displaying for having identified target in IFF system
Fruit carries out the adjustment of inhibitory effect, if false alarm rate height or point mark continuity and flocculating result are poor, it is whole that comparison provides threshold curve
Body amplitude raises range parameter Δ and the number of iterations k；
Step 82: in conjunction with false-alarm probability, point track continuity, flocculating result and inhibitory effect, comprehensive assessment false-alarm threshold curve
Inhibitory effect, the up-regulation range parameter Δ and the number of iterations k of the final false-alarm threshold curve for determining meet demand.
9. a kind of optimization equipment of the IFF system false-alarm thresholding based on polynomial curve fitting characterized by comprising
Data recording and storage device have identified target response data, root for being enrolled in IFF system covering power range in real time
It is counted according to the characterisitic parameter of every group of reply data, and is divided according to every group of response target in different direction, different distance
Class storage；
Data prediction device rejects target response data for pre-processing to the target response data of identification of storage
In boundary point amplitude variation abnormality data；
Multinomial model coefficient matrix constructing apparatus, for the data prediction according to data prediction device as a result, according to more
Item formula model, construction multinomial model coefficient matrices A become sequency spectrum or row non-singular matrix；
Fitting of a polynomial solving device, for finding out generalized inverse to the matrix A for meeting multinomial model coefficient matrix constructing apparatus
Matrix A+, and carry out fitting of a polynomial solution；
The number of iterations determining device, for determining effect most according to the difference between fitting and smoothing effect and match value and observation
Good polynomial iterative number k；
False-alarm threshold curve generating means, for generating false-alarm threshold curve；
False-alarm thresholding import and integrating apparatus, for the false-alarm threshold data to different sectors, that is, different direction, different distance into
Row sector imports and integration, forms the false-alarm threshold data in IFF system covering power range；
Effect adjustment and comprehensive evaluating device, are used under the conditions of default false-alarm probability, according to the identification target in IFF system
The requirement of point track information, the inhibitory effect of comprehensive assessment false-alarm thresholding, the final upper modulation ginseng for determining false-alarm threshold curve
Number Δ and the number of iterations k.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of method described in any item of the claim 1 to 8 is realized when being executed by processor.
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