Monopulse joint parameter estimation of multiple unresolved targets within the radar beam

3rd International Conference on Advances in Energy, Environment and Chemical Engineering

能源学;生态环境科学;化学工业

Yuan, Hui^1 ; Wang, Chunyang^1 ; An, Lei^1 ; Li, Xin^1

Air and Missile Defense College, Air Force Engineering University, Xi'an, Shanxi

710051, China^1

关键词: Conditional likelihood;    Estimated parameter;    Joint bin processing;    Joint parameter estimations;    Metropolis Hastings;    Posterior probability;    Reversible jump Markov chain Monte Carlo;    Sampling algorithm;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/69/1/012155/pdf
DOI  :  10.1088/1755-1315/69/1/012155

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Aiming at the problem of the parameter estimation of multiple unresolved targets within the radar beam, using the joint bin processing model, a method of jointly estimating the number and the position of the targets is proposed based on reversible jump Markov Chain Monte Carlo (RJ-MCMC). Reasonable assumptions of the prior distributions and Bayesian theory are adopted to obtain the posterior probability density function of the estimated parameters from the conditional likelihood function of the observation, and then the acceptance ratios of the birth, death and update moves are given. During the update move, a hybrid Metropolis-Hastings (MH) sampling algorithm is used to make a better exploration of the parameter space. The simulation results show that this new method outperforms the method of ML-MLD [11] proposed by X.Zhang for similar estimation accuracy is achieved while fewer sub-pulses are needed.