CN-116045983-B - Composite seeker interference track identification method based on interactive multi-model

Abstract

A composite seeker interference track identification method based on interactive multiple models includes initializing system parameters, assuming motion parameters of targets and interference, determining a state model for describing the motion form of the targets, modeling a multi-model system, processing the state model to obtain updated model probability and system state estimation, calculating an interference judgment threshold according to the updated model probability, judging the interference track, further identifying the interference specifically received, and finally outputting an interference track sequence number. According to the method, the motion modeling of interference is introduced into the composite seeker, the interference identification probability for gradual change of the track is effectively improved, extra high-dimensional feature calculation is not required to be introduced, interference track rejection is carried out in the track-level dimension, and the method has the advantages of low engineering realization cost, easiness in realization and the like.

Inventors

• WEN GE
• YUAN HAIFENG
• HUANG XINJUN
• ZHANG SIXING
• LIAO RUNGUI
• WU SHOULONG
• Jiang Wangkui
• FU LINGZHI
• GONG QINGKUN
• LU JIE
• YUAN FEIMA
• CAO YANG
• ZHAO LU
• WEN JING
• WEI FEN

Assignees

• 江西洪都航空工业集团有限责任公司

Dates

Publication Date

20260512

Application Date

20221229

Claims (6)

1. 1. A composite seeker interference track identification method based on an interactive multi-model is characterized by comprising the following specific steps: 1) Initializing system parameters The method comprises the steps of providing a composite seeker, wherein a plurality of sensors are installed in the composite seeker, and simultaneously detect targets moving in the same space, recording initial states of the targets, total movement time L and system sampling interval T; 2) Multi-model system modeling and interaction estimation Firstly, determining a state model for describing a target motion form, then carrying out multi-model system modeling, calculating a system state transition matrix of the state model, then determining transition probabilities of the state models, calculating a transition probability matrix among the state models, sequentially carrying out model state interaction and filtering, calculating turning rates of the state models according to filtering results, and obtaining updated model probabilities and system state estimation through an interaction process; the multi-sensor multi-model state equation is: , Wherein, the The target motion state vector representing the mth model at time k, superscript T represents the matrix transpose, Representing the position and velocity of the target in the x-axis respectively, Representing the position and velocity of the target on the y-axis, T is the sampling interval, Representing the state transition equation of the mth model at time k-1, Representing the object motion state vector of the mth model at time k-1, Representing process noise; The state model comprises a constant velocity model (CV), a cooperative turning model (CT) and a uniform acceleration model (CA), and a system state transition matrix of the model 、 、 The respective expressions are as follows: System state transition matrix of uniform velocity model Is that , System state transition matrix of cooperative turning model Is that , Wherein, the The turning rate of the target motion state at the moment k is represented, T is the sampling interval, and the state transition matrix of the cooperative turning model under the unknown turning rate is , Wherein, the Representing a turn rate state coefficient; uniform acceleration model system state transition matrix Is that , The measurement equation is , wherein, Representing the measured value of the sensor at time k, Representing the measured noise of the test signal, Representing a measurement equation; 3) Calculating interference decision threshold Calculating an interference judgment threshold according to the updated model probability obtained in the step 2), and further identifying a sensor which is particularly interfered after judging an interference track according to the calculated interference judgment threshold, wherein the method comprises the following steps of: the sensors s respectively perform interactive multi-model estimation, namely the step 2) is completed, and the model probability after the sensor s is updated at the k moment is obtained The model probability after updating is recorded as by adopting a uniform motion model and a cooperative turning model And ; B11, judging that the interference track appears First, an interference decision threshold is calculated : , Wherein, the Representing the probability of a constant velocity model CV in sensor i at time k, Representing the constant velocity model CV probability in the sensor j at the moment k; setting a threshold value Several scrambling thresholds Exceeding a threshold value The method includes the steps that at the moment, the seeker tracking is interfered, and an interference track is judged; Threshold value The value of (2) adopts the Naman-Pearson criterion, and under the condition that the judgment false alarm rate does not exceed the tolerable range, the judgment performance is optimal: , Wherein, the Representing the error function, the superscript -1 representing the reciprocal, Representative of Variance; In addition, false alarm probability is taken , Then it can be obtained B12, identifying the interference track After the interference track is determined according to the interference judgment threshold calculated in the step B11), further identifying the sensor which is particularly interfered; First, an interference release prior is determined Target mobility prior Assuming that the target is a ship moving at sea, taking the prior capability of the target machine Interference release a priori ; Model probability for sensor S, s=1, 2,..s updated model Conduct derivative and find absolute value : , Further determining sensor s model rate of change : , B121, initializing s=1, and interfering track sequence number i=s; b122:interference track judgement In combination with sensor S, s=1, 2,.. The target maneuver priori compliance and the model change rate are used for judging the interference track, and the judgment criteria are as follows: I. If it is Or (b) , , I=s; s=s+1; II. If it is Or (b) , , I=s+1; s=s+1; Repeating the steps I-II until s=S-1; Wherein, the For deciding the coefficient, take ; 4) Outputting the interference track serial number Iteration Repeating the steps 2) to 3) until And (5) after the iteration is finished, outputting an interference track sequence number.
2. 2. The method for identifying the interference tracks of the composite seeker based on the interactive multiple models according to claim 1, wherein each model transition probability is calculated by: Assuming a total of M models, each model may describe any possible target motion state, and the transition probabilities between the system models are represented by markov chain: , Wherein, the Representing the transition probability of the characterization system from model i to model j.
3. 3. The method for identifying the interference tracks of the composite seeker based on the interactive multiple models according to claim 1, wherein the state interaction of each model is carried out by adopting particle filtering, specifically: a, obtaining sample groups of each model particle If k=1 Generating an initial particle state and a corresponding weight thereof according to the known priori or detection tracking preprocessing result, and performing Gaussian approximation according to a track head initially output by a track in practical application to obtain an initialized particle Initializing the particle weight as Wherein N represents the number of sampling particles; if k >1 From known state quantity at time k-1 Covariance (covariance) Generating each model particle sample group Approximating each model particle sample to Gaussian distribution by adopting a Gaussian approximation strategy, namely adopting Gaussian fitting of particles after each iteration; , Wherein N () represents a gaussian distribution sign; b, interaction of model particles Based on given model transition probabilities Model probabilities are known State quantity and covariance Each particle is Interaction with other models results in interacted particles, expressed as , Wherein, the The update probability of each model at time k-1 is represented, The probability representing that the k moment model is m and the k-1 moment model is i is calculated , Wherein, the For normalizing constant 。
4. 4. The method for identifying the interference tracks of the composite seeker based on the interactive multiple models according to claim 1, wherein the state filtering of each model is carried out by adopting particle filtering, specifically: i, according to the state equation, the interacted particles Prediction is carried out to obtain , Ii, calculating the weight of each particle , Wherein, the The representation is proportional to the sign of the symbol, Representing likelihood functions for describing the measured density of the predicted particles, the distribution model of which is specifically related to the actual scene, assuming a Rayleigh distribution is followed, denoted as , Wherein the measurement equation is , Representing the measured value of the sensor at time k, Representing the measured noise of the test signal, Representing a measurement equation; resampling the predicted particles to obtain ; Resampling the sampled particles, i.e. for all particles, to avoid particle attenuation The weight is normalized, and then the normalized particle set is resampled, and the resampling adopts a system resampling algorithm, which comprises the following sub-steps: ① Initial initiation of , Wherein U [, ] represents a uniform distribution within the compliance interval; ② Calculation of ; ③ Calculation of ; If it is Then Returning to the step ②, otherwise, recording the nth particle at the jth position; iv State estimation of the respective models Using resampled particles , Estimating covariance matrix of each model target state by using least mean square criterion as From the filtering result, the turning rate of each model is estimated.
5. 5. The interactive multi-model based composite seeker disturbance track identification method of claim 4, wherein the turning rate estimation of each model is as follows: According to the filtering result Calculating turning rate of each state model : For the uniform model, the filtering result is The turn estimation components of the x-axis and y-axis are noted as: , The turning rate of the constant velocity model is estimated as: , For the collaborative turning model, obtained by weighting the sampled particles, i.e , Wherein, the The turning rate in each sample particle is shown.
6. 6. The method for identifying the interference tracks of the composite seeker based on the interactive multiple models according to claim 1, wherein the probability update and the system state estimation of each model are specifically as follows: updating the model probability to obtain: , Wherein, the The likelihood function representing each model filter is generally assumed to be a gaussian distribution and is calculated according to the following equation , Wherein exp [ ] represents an exponential function based on a natural constant e, Representing information describing the difference between the measured predicted value and the measured value: , Wherein, the Representing the measured value of the sensor at time k, Representing a measurement prediction value; represents a news covariance matrix for measuring uncertainty of the news: , calculating the correct probability model of each model And then, weighting the state estimation when each model is correct to obtain the system state estimation: , Similarly, the turning rate estimation: 。

Description

Composite seeker interference track identification method based on interactive multi-model Technical Field The invention relates to the technical field of guidance control, in particular to a composite seeker interference track identification method based on an interactive multi-model. Background With the continuous upgrading of battlefield environmental interference attack and defense countermeasure, battlefield actual combat increasingly tends to develop to combined interference, and the difficulty of interference release measures and countermeasure means is spirally increased. For seeker detection, the interference measure and seeker detection feature is generally a continuous process of countering upgrades, and single mode seekers have become increasingly unable to meet the increasingly complex interference countermeasures demands of the future. The composite seeker adopts a multi-sensor composite guidance system, two or more modes are combined in one seeker, and the characteristics of the multi-sensor are utilized to complement each other and complement the advantages, so that the overall anti-interference performance of the seeker is improved, and the seeker has become one of the key measures of modern war anti-interference. Aiming at the complex system anti-interference technology, how the seeker correctly eliminates the interference source has decision significance for combat impact, the interference elimination of the seeker can be considered from multiple levels, such as a signal level, a feature level, a track level, a decision level and the like, and the interference track elimination in the track level dimension has the advantages of low engineering realization cost, easiness in realization and the like. In the document 'composite seeker target information fusion anti-interference technology research [ J ]. Flight control and detection, 2018', a measurement value association degree accumulated in a time domain is used as an interference track judgment criterion, and in the document 'radar/infrared composite seeker anti-interference tracking method [ J ]. Flight mechanics, 2016', interference is judged by using an observation information association degree and an innovation variance track value. From the perspective of track level information fusion anti-interference, aiming at the existing interference patterns and motion forms, the interference tracks can be divided into two types of track mutation and track gradual change, the track level anti-interference method of the existing composite system mainly considers observed information, has a certain recognition rate on the interference of the track mutation, but has a certain application limitation on the interference recognition of the track gradual change, such as centroid type interference, in the process of tracking the movement of the interference tracks and the target tracks, the judgment accuracy of the variance judgment of the information on the interference tracks is greatly reduced. Disclosure of Invention The invention aims to provide a composite seeker interference track identification method based on an interactive multi-model so as to solve the problems in the background technology. The technical problems solved by the invention are realized by adopting the following technical scheme: a composite seeker interference track identification method based on interactive multiple models comprises the following specific steps: 1) Initializing system parameters The method comprises the steps of providing a composite seeker, wherein a plurality of sensors are installed in the composite seeker, and simultaneously detect targets moving in the same space, recording initial target states, total moving time and system sampling intervals T; 2) Multi-model system modeling and interaction estimation Firstly, determining a state model for describing a target motion form, then carrying out multi-model system modeling, calculating a system state transition matrix of the state model, determining transition probabilities of the state models, calculating a state model transition probability matrix, sequentially carrying out model state interaction and filtering, calculating turning rates of the state models according to filtering results, and obtaining updated model probabilities and system state estimation through an interaction process; 3) Calculating interference decision threshold Calculating an interference judgment threshold according to the updated model probability obtained in the step 2), and further identifying a sensor which is particularly interfered after judging that an interference track appears according to the calculated interference judgment threshold; 4) Outputting the interference track serial number And (3) repeating the steps 2) to 3) until the interference is found, and outputting an interference track sequence number after iteration is finished. In the present invention, the multi-sensor multi-model state equation is: Wherein, the