Multi-Static Passive Coherent Location Method Based On Belief Propagation

Passive coherent location(PCL)systems locate targets by receiving direct wave signals emitted by illuminators and reflected signals radiated to targets,which have advantages such as low cost,small equipment size,strong stealth and anti-stealth capabilities,and has important applications in military and civil fields.The multi-static PCL system utilizes the spatial distribution diversity of multiple receivers and illuminators to detect targets from different angles,which further improves the antistealth capability of the system.Traditional multi-static PCL algorithms have high computational complexity,large storage requirements,and poor scalability when tracking multiple targets.When multiple illuminators operate in single frequency network,the problem will be more severe.To address the above problems,this paper develops a study of multi-static PCL methods based on belief propagation.The main work is as follows:1.In order to solve the problem that the traditional multi-static PCL algorithm has poor scalability in terms of the number of targets and base station,a Gaussian belief propagation algorithm is proposed.Firstly,the mean and covariance of Gaussian distribution are used to describe the message passed in the factor graph.Then,generalized virtual measurement is introduced to unify the representation of the likelihood function.Finally,the first term of Taylor series expansion is used to approximate the nonlinear function in PCL system.This method overcomes the problem of excessive computational complexity at the filter level of the belief propagation due to the use of random samples or Gaussian mixture density to approximate the probability distribution.The computational complexity of the proposed method grows linearly with the number of measurements and quadratically with the number of targets.Simulation results show that the proposed algorithm has good scalability in terms of the number of targets and measurements.2.A multi-association Gaussian belief propagation algorithm is proposed for the3 D data association uncertainty problem in multi-static PCL operating in single frequency network.The illuminator-target-oriented and measurement-oriented association variables are proposed for the first time to describe the 3D data association uncertainty,and on this basis,the joint posterior probability density function and its factorization are derived for this passive location problem to obtain the corresponding factor graph.The computational complexity of the method increases quadratically with the number of illuminators.The simulation results show that the proposed algorithm can effectively solve the 3D data association uncertainty problem.3.To address the problem of tracking accuracy degradation due to receiver location errors,a multi-association Gaussian belief propagation with receiver location uncertainty algorithm is proposed.By modelling the receiver state as a random vector,the joint posterior probability density function of the joint target and receiver location problem and its factorization are derived to obtain the corresponding factor graph.The increase in the number of receivers does not affect the computational complexity of the algorithm in terms of the number of measurements,targets and illuminators.The simulation results show that the proposed algorithm can effectively solve the problem of tracking accuracy degradation due to receiver location uncertainty.