Research On Multiple Targets Tracking Technology Of Frequency Diversity Array Radar

The radar target tracking technology is a key technology in military fields such as battlefield intelligence reconnaissance,combat environment perception,and precise guidance of weapons and equipment,as well as key technologies in civilian field navigation and civil drone monitoring.It is also an indispensable technology to ensure the safety of my country’s land.The frequency diversity array(FDA)radar is a new system radar that has emerged in recent years.The FDA can be combined with multiple inputs and multiple outputs(MIMO)system.By using diversity technology,it can form an equivalent range-angle beampattern.The characteristics of this difference from the traditional phase array(PA)enable the FDA-MIMO radar to effectively fight the main-lobe deceptive interference.This feature of FDA-MIMO radar with range information has the potential to improve anti-interference,target detection and tracking performance.Based on the target tracking technology,this dissertation studies the relevant tracking algorithms in detail.In addition,this dissertation uses the FDA-MIMO radar combined target tracking algorithm to achieve the FDA-MIMO radar target tracking and cognitive anti-interference.The main work and innovation are as follows:(1)Aiming at the target tracking problem of the lack of prior information(target detection probability,background clutter density,target priority location information),a robust measurement-driven cardinality balance multi-target multi-Bernoulli(RMD-CBMeMBer)filter is proposed.Establish a target state transfer and point measurement model,break through the restrictions of the lack of prior information,and derive the closed-type solution of the RMD-CBMeMBer filter.Eventually,the adaptive newborn and the maintenance of the target are achieved.(2)To address the issue of the RMD-CBMeMBer filter cannot output the multiple targets trajectories,the measurement-driven adaptive δ-generalized labeled multi-target multibernoulli(δ-GLMB)filter is proposed.Firstly,the labeled associated likelihood function is derived to broke through the technical problem of correlation between target state and measurement in label scenarios.On this basis,the adaptive capture of the target and the output of each target trajectory are achieved by combining label technology.In addition,the generalized likelihood function(GLF)and box particle likelihood function are introduced,and the application of point particles and box particles are given to the robust tracking and the trajectories output of multiple targets.(3)In order to solve the problem of unknown and non-standard measurement(image measurement)in the traditional tracking algorithm,an improved labeled multi-target multiBernoulli(I-LMB)filter with FDA-MIMO radar is proposed.Chapter 4 first reveals the similarity features between the traditional point measurement likelihood function and the multiple signal classification(Music)power spectrum,and establishes a likelihood function based on the Music power spectrum by utilizing the range dimension controllable degree of freedom(DOF)of FDA-MIMO radar,breaking through the limitations of target detection and traditional measurement likelihood functions.Secondly,the analytical solution of the I-LMB filter was derived,and a sequential Monte Carlo(MC)implementation of the I-LMB filter was given.Finally,stable tracking of multiple targets was achieved.(4)A cognitive FDA-MIMO radar deception interference suppression and target detection algorithm based on sample selection is proposed to address issues such as main-lobe deception interference suppression,tracker error,and non-uniform sample selection.Combining tracking algorithms in the FDA-MIMO system,a closed-loop cognitive structure of FDAMIMO is formed.By utilizing the target prediction information provided by the tracker,an adaptive sample selection(ASS)method is proposed.Secondly,an adaptive sample selection MVDR beamformer(MVDR-ASS)and an adaptive sample selection robust beamformer(Robust-ASS)based on the maximum signal to interference plus noise ratio criterion(MSINR)were proposed based on the ASS algorithm,and a cognitive FDA-MIMO radar deception interference suppression and target detection system has been constructed.Ultimately,cognitive main-lobe(side-lobe)deception interference suppression,target detection,and target tracking were achieved.(5)A cognitive FDA-MIMO radar network target identification and tracking algorithm is proposed to address issues such as difficulty in target identification,lack of prior target information,easy divergence of trackers,and errors in tracker prediction states.This algorithm is based on the presence of main-lobe deception trajectory interference.Firstly,by analyzing the spatial distribution characteristics of targets and deceptive interference in networked radar scenarios,an FDA-MIMO radar target network identification algorithm is proposed,which achieves the identification of true targets;Secondly,the mechanism of tracking divergence in extended Kalman filter(EKF)tracker was revealed,and the target state initialization strategy driven by G-pair measurements with large time interval(G-PMLTI)and target state correction(TSC)algorithms based on auxiliary particles were proposed to realize robust prediction of the target state;Finally,a virtual guidance vector optimization strategy based on the criterion of maximum Capon power spectrum(CMCP)principle was proposed,which improved interference suppression performance and achieved target identification and robust tracking in cognitive FDA-MIMO radar networks.