基于改进粒子滤波的微弱信号检测与跟踪

发布时间：2018-11-10 21:32

【摘要】：近年来,微弱信号的检测和跟踪技术被广泛的应用在工业、交通和国防等领域,但随着对检测和跟踪的精度要求越来越高,微弱信号分离的难题也日益突显。基于此,本文提出基于改进粒子滤波的检测前跟踪方法,在低信噪比下实现对微弱目标的精确跟踪。首先,本论文对检测前和检测后两种跟踪方法优势和不足进行论述,对无源传感器的观测模型和目标运动模型进行建模,并且详细的介绍了贝叶斯估计和在其框架下的粒子滤波理论,引出粒子滤波检测前跟踪方法的优越性,为后续研究提供了理论依据。其次,介绍了传统的粒子滤波检测前跟踪算法,并对模型进行验证。但是由于传统算法自身的缺陷导致粒子分布不均及多样性不足,介绍了几种常用的改进算法。并在改进算法的基础上,将进化计算中的交叉与变异操作引入到蒙特卡罗算法中,在重采样过程中引入Metropolis-Hastings (MH)重采样方法,该算法在一定程度上改善了粒子多样性匮乏的问题并降低了算法的运行时间。仿真结果证明改进后的拟蒙特卡罗智能粒子滤波算法的效率和追踪精确度都大幅度提高。最后,针对微弱目标做匀加速和转弯运动的检测与跟踪问题,在改进后的拟蒙特卡罗智能粒子滤波算法的基础上提出多模型结合的建模方法,并在此基础上提出基于改进的拟蒙特卡罗智能粒子滤波交互式多模型检测前跟踪算法对模型进行优化。仿真结果表明,该改进算法在保证跟踪精度的前提下能在一定程度上降低粒子数,能精确的对匀加速和转弯运动的微弱目标进行跟踪。证明了改进后的拟蒙特卡罗智能粒子滤波算法对微弱目标检测和跟踪的有效性和可靠性。
[Abstract]:In recent years, weak signal detection and tracking technology has been widely used in industry, traffic, national defense and other fields, but with the higher accuracy of detection and tracking, the difficult problem of weak signal separation has become increasingly prominent. Based on this, a pre-detection tracking method based on improved particle filter is proposed to accurately track weak targets at low signal-to-noise ratio (SNR). Firstly, this paper discusses the advantages and disadvantages of the two tracking methods before and after detection, and models the observation model and the target motion model of the passive sensor. The Bayesian estimation and particle filter theory under the framework of Bayesian estimation are introduced in detail, and the superiority of particle filter detection before tracking method is introduced, which provides a theoretical basis for further research. Secondly, the traditional particle filter detection before tracking algorithm is introduced, and the model is verified. However, due to the defects of the traditional algorithm itself, the particle distribution is uneven and the diversity is insufficient, so several commonly used improved algorithms are introduced. On the basis of the improved algorithm, the crossover and mutation operations in evolutionary computation are introduced into Monte Carlo algorithm, and the Metropolis-Hastings (MH) resampling method is introduced in the process of resampling. To some extent, the algorithm improves the lack of particle diversity and reduces the running time of the algorithm. The simulation results show that the efficiency and tracking accuracy of the improved quasi-Monte Carlo intelligent particle filter algorithm are greatly improved. Finally, aiming at the problem of detecting and tracking weak targets with uniform acceleration and turning motion, a multi-model combined modeling method is proposed based on the improved quasi-Monte Carlo intelligent particle filter algorithm. On this basis, an improved Quasi-Monte Carlo intelligent particle filter interactive multi-model detection pre-tracking algorithm is proposed to optimize the model. The simulation results show that the improved algorithm can reduce the number of particles to a certain extent and accurately track the weak targets with uniform acceleration and turn motion under the premise of ensuring the tracking accuracy. It is proved that the improved quasi-Monte Carlo intelligent particle filter algorithm is effective and reliable for weak target detection and tracking.
【学位授予单位】：山东科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212.9;TN713

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