Research On Target Tracking Technology In External Illuminator Passive Radar

Traditional active radar is vulnerable to enemy interference and investigation in modern electronic warfare.External illuminator passive radar utilizes non-cooperative illuminators of opportunity as its transmitters,and deals with the direct wave received from the illuminator and the echo wave reflected by the target through coherent processing to detect and track targets.External illuminator passive radar has good concealment and anti-detection performance due to the application of the illuminators of opportunity.Therefore,it has a significant effect in modern electronic warfare and causes widespread attention around the world.This dissertation mainly focuses on the target tracking technology in external illuminator passive radar in order to meet the requirements of a specified project.The main research work and contributions are outline as follows:1.The probability multiple hypothesis tracker(PMHT)algorithm is discussed in the target number of known and fixed scenarios.An adaptive PMHT algorithm is proposed to solve the problem of non-adaptivity,narcissism and over-hospitality to clutter in traditional PMHT algorithm.Firstly,the proposed method analyses why traditional algorithm performance is limited and provides a clear direction to subsequent improvement.Then the posterior association probability between targets and measurements,which updates to estimated results adaptively,is revised by adding a state covariance term in conditional probability.Thus,the new method has adaptivity and self-correction.Finally,in allusion to the special hypothesis in PMHT,the posterior association probability is revised again by combining join probability data association(JPDA)algorithm to solve the problem of clutter vulnerability.The simulation results demonstrate that the proposed algorithm improves the problem in original method and keeps a linear complexity but a near tracking performance to JPDA algorithm.2.A random finite sets(RFS)based probability hypothesis density(PHD)filter is discussed in the target number of unknown scenarios.A measurement driven PHD algorithm is proposed to solve the problem that newborn target density is necessary in traditional PHD filter.Firstly,a gate technology is used to distinguish the measurements into two categories.The exist measurements are used to update the state of exist targets while the newborn measurements are used to detect new targets.Then,the track initiation is applied on newborn measurements to initiate track.Once a track is confirmed,a new target is declared and the posterior density of new target is constructed according to the initial tracks.The simulation results demonstrate that the proposed algorithm can detect and track new targets even though the information of new target is unknown.3.A PHD based track before detect(TBD)algorithm is discussed in the target number of unknown scenarios.Signal reflected from target is always weak in external illuminator passive radar and TBD algorithm,which doesn't need a threshold judgement,can realize the weak target detection and tracking through dealing with the original measurements directly.A PHD based TBD algorithm is proposed in this paper to track weak target in external illuminator passive radar.Firstly,the likelihood function of measurement is derived under the passive radar circumstance.Then a track before detect algorithm based on PHD is proposed and its particle implementation is introduced later.Finally,the proposed method is analyzed in a simulation and the results demonstrate that the performance of TBD algorithm is superior to the traditional track after detect PHD algorithm.