On The Particle Filter Algorithm And Its Circuit Implementation

Nonlinear filtering widely exists in the statistical signal processing areas, such as target tracking, infrared dim target detection, navigation, fault detection and finance. As an effective method for Nonlinear/NonGaussian dynamic system, particle filter has shown its application prospect in both military and civil area.This dissertation is an exploration on the critical issue of particle filter about the importance function to sample particles. The new importance sampling methods are proposed to improve the performance of particle filter by compensation and optimization strategy. Nonparametric estimation is also introduced to approximate the posterior by moving particles to seek the posterior model. Otherwise, in order to reduce the computationally intensive in implementation, we also design the circuit of particle filter and its parallel structure. These can reduce the resource utilization and enhance the computation efficiency. The summary of the thesis are as follows:1. A compensated extended Kalman particle filter is proposed. The linearization error of the extended Kalman filter is analyzed firstly. Then a compensation method with adjustable factor is introduced to minimize the linearization error and to improve the generated proposal distribution in particle filtering. Meanwhile, the new proposal distribution integrates the latest observation. Particles sampled from this distribution are closer to the true distribution. The new method performed better than the standard particle filter (PF) and the Kalman particle filter. At the same time, the complexity of the proposed algorithm is lower than the unscented particle filter.2. An optimization-based particle filter is proposed. It is difficult to choose an appropriate adjustable factor in the compensated extended Kalman particle filter. The new algorithm provided an optimization-based method (the steepest descent method) to generate the parameters of the importance function iteratively. High quality particles are sampled form the proposal distribution with the integration of the latest observation. The estimation precision and the sampling efficiency are improved by the method.3. A variable bandwidth kernel particle filter is proposed (VBKPF). It is difficult to find an analytical expression of the posterior. However, the traditional particle filter samples from an importance density function with an unknown prior. The kernel density estimation (KDE) is invoked in the new algorithm with the framework of the particle filter. We adopt the plug-in method to get the global fixed bandwidth by optimizing the asymptotic mean integrated squared error firstly. Then, particle-driven bandwidth selection is introduced in the KDE. To get a more effective allocation of the particles, we use the variable bandwidth KDE to approximate the posterior probability density functions (PDFs) by moving particles toward the posterior. This gives a closed-form expression of the true distribution. The proposed VBKPF performs better than the standard particle filter and the kernel particle filter both in efficiency and estimation precision.4. A covariance based variable bandwidth kernel particle filter is proposed to reduce the complexity of the VBKPF. Firstly, covariance matrix of the particle sets is used to compute the coarse bandwidth and the posterior probability density functions. Then, each particle can acquire its own accurate bandwidth by adjusting the global kernel bandwidth to improve the precision of the KDE estimation. To improve the estimation precision, the iteration strategy is used to seek the posterior model by moving particles toward the posterior.5. The circuit of the standard particle filter in bearing-only tracking system is designed. We present an efficient resampling architecture by redundant storage strategy to deal with the particle coverage problem. The Block RAM resource is reduced greatly with this scheme. By using the redundant storage strategy appropriately, we also design the parallel structure of the particle filter. High quality particles are exchanged between the redundant storage RAMs to eliminate the imbalance phenomenon. The new parallel structure can reduce the resource utilization and the executive time. It also can improve the operation efficiency of the algorithm.