Poboljšani FastSLAM2.0 algoritam korištenjem ANFIS-a i PSO-a

FastSLAM2.0 is a framework for simultaneous localization of robot using a Rao-Blackwellized particle filter (RBPF). One of the problems of FastSLAM2.0 relates to the design of RBPF. The performance and quality of the estimation of RBPF depends heavily on the correct a priori knowledge of the process and measurement noise covariance matrices that are in most real-life applications unknown. On the other hand, an incorrect a priori knowledge may seriously degrade their performance. This paper presents an intelligent RBPF to solve this problem. In this method, two adaptive Neuro-Fuzzy inference systems (ANFIS) are used for tuning the process and measurement noise covariance matrices and for increasing acuuracy and consistency. In addition, we use particle swarm optimization (PSO) to optimize the performance of sampling. Experimental results demonstrate that the proposed algorithm is effective.FastSLAM2.0 je algoritam za istodobnu lokalizaciju robota i kartiranje prostora koji koristi Rao-Blackwell verziju čestičnog filtra (RBPF). Jedan od problema FastSLAM2.0 algoritma je u dizajnu samog RBPF-a. Performanse i kvaliteta estimacije RBPF-a značajno ovisi o apriori poznavanju procesa i matrica kovarijanci mjernog šuma koje su za većinu procesa iz stvarnog svijeta nepoznate. S druge strane pogrešno pretpostavka može značajno narušiti performanse. Ovaj rad predstavlja inteligentnu verziju RBPF-a koja rješava ovaj problem. Predstavljena metoda koristi dva adaptivna neizrazito-neuronska sustava (ANFIS) za podešavanje matrica kovarijanci procesnog i mjernog šuma čime se povećava točnost i konzistencija RBPF algoritma. Također koristi se i optimizacija roja čestica (PSO) za optimiziranje performansi otipkavanja. Eksperimentalni rezultati pokazuju efikasnost predloženog algoritma

Target tracking in MIMO Radar Systems using Interactive Multiple Extended Kalman Filter and its Optimization

Multi-Input and Output Radar System (MIMO) is a new type of radar system that has been the subject of much research in recent years due to its many advantages. Meanwhile, the issue of target tracking in MIMO radar systems is of great importance, and providing an efficient solution for it remains an unresolved issue. In this paper, target tracking in MIMO radar systems using optimizing Kalman interactive filter is investigated. The proposed method in this research is the MIMO radar system for the simultaneous tracking of multiple targets. In the assumed system model, the interactive multiple model (IMM) based on Extended Kalman Filter (EKF) is used to understand the effective tracking of the target. Also, the optimization of the multi-objective tracking model in MIMO radar systems has been done by the proposed method using the particle swarm optimization (PSO) algorithm. This optimization algorithm estimates the fit of each studied response based on the number of energy resources and time consumed. The efficiency of the proposed method in a simulated environment has been evaluated and its performance in tracking multiple targets has been investigated in terms of different criteria. Based on the results of these experiments, the proposed method, in addition to reducing the amount of tracking error, can be effective in reducing energy consumption and reducing the sampling period. Thus, the proposed method will reduce resource consumption in the multi-objective tracking system

An adaptive particle filter based on PSO and fuzzy inference system for nonlinear state systems

Particle filters have been widely used in nonlinear/non-Gaussian Bayesian state estimation problems. However, the particle filter (PF) is inconsistent over time. The inconsistency of PF mainly results from the particle depletion in resampling step and an incorrect priori knowledge of process and measurement noise. To cope with this problem and enhance the accuracy and consistency of the state estimation, an adaptive particle filter(APF) is proposed in this paper. In APF, an adaptive fuzzy square-root unscented Kalman filter (AFSRUKF) is used to generate the proposal distribution. This causes that beside the merit of reducing the computational cost, APF has some other advantages such as increasing consistency that leads to more numerical stability and better performance. Moreover,APF can work in unknown statistical noise behaviour and is more robust. This is why the fuzzy inference system (FIS) supervises the performance of square-root unscented particle filter (SRUPF) using tuning statistical noises. In APF, to increase the diversity of particles, the resampling process is done based on the particle swarm optimization (PSO). With this resampling strategy, the small-weight particles are modified to the large-weight ones without duplication and elimination of particles. The effectiveness of APF is demonstrated by using two experiment examples through Monte Carlo simulations. The simulation results demonstrate the effectiveness of the proposed method