Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

The aim of this paper is to propose an algorithm for particle filter which will overcome its problem of particle impoverishment. Our approach embed cuckoo search via levy flight algorithm into standard particle filter for Non-linear and Non-Gaussian state estimation. The use of cuckoo search via levy flight optimization overcomes the problem of particle impoverishment which is generated during resampling. To validate the efficacy of the proposed algorithm, its performance is compared with the particle filter and PSO Particle Filter (PSO-PF). Simulation results for generic one dimensional problem and two dimensional classic bearing only tracking problem show that our novel Cuckoo-PF outperforms other algorithms when RMSE, robustness and sample impoverishment are considered as metric for performance measurement.

Particle filter based on cuckoo search for Non-linear state estimation

Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm.

/pdf/evolutionary-computing-and-particle-filtering-a-hardware-3wc40xy6mn.pdf

Evolutionary Computing and Particle Filtering: A Hardware-Based Motion Estimation System

This paper presents the Optimized Kalman Particle Swarm (OKPS) filter. This filter results from two years of research and improves the Swarm Particle Filter (SPF). The OKPS has been designed to be both cooperative and reactive. It combines the advantages of the Particle Filter (PF) and the metaheuristic Particle Swarm Optimization (PSO) for ego-vehicles localization applications. In addition to a simple fusion between the swarm optimization and the particular filtering (which leads to the Swarm Particle Filter), the OKPS uses some attributes of the Extended Kalman filter (EKF). The OKPS filter innovates by fitting its particles with a capacity of self-diagnose by means of the EKF covariance uncertainty matrix. The particles can therefore evolve by exchanging information to assess the optimized position of the ego-vehicle. The OKPS fuses data coming from embedded sensors (low cost INS, GPS and Odometer) to perform a robust ego-vehicle positioning. The OKPS is compared to the EKF filter and to filters using particles (PF and SPF) on real data from our equipped vehicle.

/pdf/okps-a-reactive-cooperative-multi-sensors-data-fusion-4jnd3mbbm1.pdf

OKPS: A Reactive/Cooperative Multi-Sensors Data Fusion Approach Designed for Robust Vehicle Localization

A target of interest may exhibit significant appearance variations because of its complex maneuvers, ego-motion of the camera platform, etc. Currently, target tracking in forward-looking infrared (FLIR) sequences is still a challenging problem in the field of computer vision. Although many efforts have been devoted, there are still some issues to be addressed. First, state particles generated by prior information cannot approximate the probability density function well when the target state changes obviously. Second, plenty of particles have to be employed to obtain satisfying estimation of target state which will cause heavy computational burden in turn. In this paper, a hierarchical search strategy (HS tracker) is proposed to track infrared target in the particle filter framework, and there are two observation models employed to locate the target robustly. In the first stage, a saliency map leads the redistributed state particles to cover the salient areas that can provide a rough prediction of the target areas. In the second stage, sparse representation is employed to search for a subset of true ones from all the target candidates; thus, only efficient state particles are used to estimate the target state. The proposed method is tested on numerous FLIR sequences from the US army aviation and missile command database, and experimental results demonstrate the excellent performance.

Hierarchical search strategy in particle filter framework to track infrared target

Particle filters (PF) are widely used for state estimation in non-linear and non-Gaussian environments. However, conventional particle filters possess some drawbacks such as sample impoverishment and sample size dependency. In this paper, a novel parallel hybrid evolutionary particle filter is proposed to solve those problems from the perspective of evolutionary computation. In the proposed algorithm, an effort has been made to fuse a genetic algorithm (GA) and particle swarm optimization (PSO) together to improve the standard particle filter. Genetic operators such as crossover and mutation are utilized to maintain the particle diversity and PSO is used to optimize the particle distribution. A parallel scheme is employed to reduce the computation time so it is more suitable to implement by multithreaded programming for real-time system. The simulation results demonstrate the effectiveness of the proposed algorithm.

A Parallel Hybrid Evolutionary Particle Filter for Nonlinear State Estimation

With improvement in the quality of people's life and the change of their lifestyle, the demand for intelligent water dispensers is increased. This study, therefore, proposes to de-sign a service robot that can not only move autonomously but also serve people intelligently. In this paper, a solution to de-sign an intelligent water-dispensing service robot is proposed. A new integrated system architecture is introduced, and a two-stage localization, a novel method of indoor robot localization, is described to solve the localization problem of the robot. Ow-ing to the detailed description of the implementation, it is clearly indicated that the proposed solution is reasonable and feasible. Besides, comparisons of various indoor localization methods and indoor robot applications demonstrate that the water-dispensing service robot has advantages over the existing designs and application.

Jialong Zhang

Papers

A Parallel Hybrid Evolutionary Particle Filter for Nonlinear State Estimation

Indoor Water-Dispensing Robot with Two-Stage Localization

Joint depth map super-resolution method via deep hybrid-cross guidance filter