Jung Min Pak

Improving Reliability of Particle Filter-Based Localization in Wireless Sensor Networks via Hybrid Particle/FIR Filtering

The need for accurate, fast, and reliable indoor localization using wireless sensor networks (WSNs) has recently grown in diverse areas of industry. Accurate localization in cluttered and noisy environments is commonly provided by means of a mathematical algorithm referred to as a state estimator or filter. The particle filter (PF), which is the most commonly used filter in localization, suffers from the sample impoverishment problem under typical conditions of real-time localization based on WSNs. This paper proposes a novel hybrid particle/finite impulse response (FIR) filtering algorithm for improving reliability of PF-based localization schemes under harsh conditions causing sample impoverishment. The hybrid particle/FIR filter detects the PF failures and recovers the failed PF by resetting the PF using the output of an auxiliary FIR filter. Combining the regularized particle filter (RPF) and the extended unbiased FIR (EFIR) filter, the hybrid RP/EFIR filter is constructed in this paper. Through simulations, the hybrid RP/EFIR filter demonstrates its improved reliability and ability to recover the RPF from failures.

Switching Extensible FIR Filter Bank for Adaptive Horizon State Estimation With Application

Horizon size is an important parameter that affects the estimation performance of finite impulse response (FIR) filters. In this brief, we propose a novel adaptive horizon approach that aims to adapt the horizon size at each time point. The approach suggests providing state estimation using a bank of FIR filters called the switching extensible FIR filter bank (SEFFB), which consists of several FIR filters operating using different horizon sizes. The horizon sizes and the number of FIR filters in the SEFFB are adapted to changes in system characteristics using maximum likelihood. The SEFFB is applied to target tracking using a ground moving target indicator. A significant performance improvement is demonstrated using the SEFFB in comparison with a single FIR filter using constant optimal horizon size.

Distributed Hybrid Particle/FIR Filtering for Mitigating NLOS Effects in TOA-Based Localization Using Wireless Sensor Networks

For indoor localization based on wireless sensor networks, the transmission of wireless signals can be disrupted by obstacles and walls. This situation, called non-line-of-sight (NLOS), degrades localization accuracy and may lead to localization failures. This paper proposes a new NLOS identification algorithm based on distributed filtering to mitigate NLOS effects, including localization failures. Rather than processing all measurements via a single filter, the proposed algorithm distributes the measurements among several local filters. Using distributed filtering and data association techniques, abnormal measurements due to NLOS are identified, and negative effects can be prevented. To address cases of localization failures due to NLOS, the hybrid particle finite impulse response filter (HPFF) was adopted. The resulting distributed HPFF can self-recover by detecting failures and resetting the algorithm. Extensive simulations of indoor localization using time of arrival measurements were performed for various NLOS situations to demonstrate the effectiveness of the proposed algorithm.

Unbiased Finite-Memory Digital Phase-Locked Loop

Digital phase-locked loops (DPLLs) have been commonly used to estimate phase information. However, they exhibit poor performance or, occasionally, a divergence phenomenon, if noise information is incorrect or if there are quantization effects. To overcome the weaknesses of existing DPLLs, we propose a new DPLL with a finite-memory structure called the unbiased finite-memory DPLL (UFMDPLL). The UFMDPLL is independent of noise covariance information, and it shows intrinsic robustness properties against incorrect noise information and quantization effects due to the finite-memory structure. Through numerical simulations, we show that the proposed DPLL is more robust against incorrect noise information and quantization effects than the conventional DPLLs are.

Self-recovering extended Kalman filtering algorithm based on model-based diagnosis and resetting using an assisting FIR filter

This paper proposes a new intelligent filtering algorithm called the self-recovering extended Kalman filter (SREKF). In the SREKF algorithm, the EKFs failure or abnormal operation is automatically diagnosed using an intelligence algorithm for model-based diagnosis. When the failure is diagnosed, an assisting filter, a nonlinear finite impulse response (FIR) filter, is operated. Using the output of the nonlinear FIR filter, the EKF is reset and rebooted. In this way, the SREKF can self-recover from failures. The effectiveness and performance of the proposed SREKF are demonstrated through two applications - the frequency estimation and the indoor human localization.

Multi-target FIR tracking algorithm for Markov jump linear systems based on true-target decision-making

Most existing multi-target tracking (MTT) algorithms are based on Kalman filters (KFs). However, KFs exhibit poor estimation performance or even diverge when system models have parameter uncertainties. To overcome this drawback, finite impulse response (FIR) filters have been studied; these are more robust against model uncertainty than KFs. In this paper, we propose a novel MTT algorithm based on FIR filtering for Markov jump linear systems (MJLSs). The proposed algorithm is called the multi-target FIR tracking algorithm (MTFTA). The MTFTA is based on the decision-making process to identify the true-targets state among candidate states. The true-target decision-making process utilizes the likelihood function and the Mahalanobis distance. We show that the proposed MTFTA exhibits better robustness against model parameter uncertainties than the conventional KF-based algorithm.

Accurate and Reliable Human Localization Using Composite Particle/FIR Filtering

The particle filter (PF) is a popular filtering algorithm in various localization problems represented by nonlinear state-space models. Although the PF can provide accurate localization results, it often fails in localization because of the sample impoverishment phenomenon. In this paper, we propose a novel nonlinear filtering method that combines a PF with a robust filter, called a finite impulse response (FIR) filter, in order to accomplish accurate and reliable localization. The proposed filter is called the composite particle/FIR filter (CPFF). In the CPFF framework, the PF is the main filter used in normal situations. When PF failures occur, the FIR filter is used to recover the PF from failures. To detect PF failures, a new decision-making algorithm is proposed in this paper. The proposed CPFF is applied to indoor human localization using a wireless sensor network. The CPFF is accurate and reliable under conditions in which the pure PF typically exhibits degraded accuracy or failures in localization.

Fuzzy horizon group shift FIR filtering for nonlinear systems with TakagiSugeno model

In recent years, the TakagiSugeno (TS) fuzzy model has been commonly used for the approximation of nonlinear systems. Using the TS fuzzy model, nonlinear systems can be converted into linear time-varying systems, which can reduce approximation errors compared with the conventional Taylor approximation. In this paper, we propose a new nonlinear filter with a finite impulse response (FIR) structure based on the TS fuzzy model. We firstly derive the fuzzy FIR filter and combine it with the horizon group shift (HGS) algorithm to manage the horizon size, which is an important design parameter of FIR filters. The resulting filter is called the fuzzy HGS FIR filter (FHFF). Due to the FIR structure, the FHFF has robustness against model parameter uncertainties. We demonstrate the performance of the FHFF in comparison with existing nonlinear filters, such as the fuzzy Kalman filter and the particle filter.

Maximum likelihood FIR filter for visual object tracking

Visual object trackers usually adopt filters, such as the Kalman filter (KF) and the particle filter (PF), in order to improve tracking accuracy by suppressing measurement noises. However, if the filters have infinite impulse response (IIR) structures, the visual trackers adopting them can exhibit degraded tracking performance when system models have parameter uncertainties or when the noise information is incorrect. To overcome this problem, in this paper, we propose a new finite impulse response (FIR) filter for visual object tracking (VOT). The proposed filter is derived by maximizing the likelihood function, and it is referred to as the maximum likelihood FIR filter (MLFIRF). We conducted extensive experiments to show that the MLFIRF provides superior and more reliable tracking results compared with the KF, PF, and H filter (HF) in VOT. HighlightsAn alternative visual object tracker, called the maximum likelihood FIR tracker (MLFIRT), is proposed.The MLFIRT has the special robustness against model parameter uncertainties and incorrect noise information.Experimental results using the MLFIRT and the conventional visual trackers are presented.The experiments compare the MLFIRT with the Kalman tracker (KT) and the H-infinity tracker (HT).The MLFIRT provides superior and more reliable tracking results than the KT and the HT.

Particle filtering approach to membership function adjustment in fuzzy logic systems

The fuzzy logic system has been a popular tool for modeling nonlinear systems in recent years. In the fuzzy logic system, the shape of the membership function has a significant effect on the modeling accuracy. Thus, membership function adjustment methods have been studied and developed. However, in highly nonlinear systems, the existing membership function adjustment method based on the extended Kalman filter (EKF) may exhibit poor performance due to the linearization error. In this paper, to overcome the drawback of the EKF-based membership function adjustment (EKFMFA), we propose a new membership function adjustment method based on the particle filter (PF). The proposed PF-based membership function adjustment (PFMFA) does not suffer from performance degradation due to the linearization error. We demonstrate that the PFMFA outperforms the EKFMFA through the simulation of a fuzzy cruise control system.