Error-Ellipse-Resampling-Based Particle Filtering Algorithm for Target Tracking

In the application of particle filter algorithm for target tracking in wireless sensor networks, an Auxiliary particle filter (APF) and Gaussian particle filter (GPF) are discussed to solve the particle degradation problem of Particle Filter (PF). By introducing an auxiliary variable and two rounds weighted processes, APF makes the particle weights stable and relaxes particle degradation. GPF uses a Gaussian density function to approximately estimate posterior probability distribution. It doesn’t require re-sampling, and there is no particle degradation. Therefore, three algorithm are applied to single-target tracking in wireless sensor networks. Finally, the comparison of three algorithm’s performance in target tracking is presented and the simulation results are also given. From these results we can see that APF has better accuracy than PF algorithm and GPF has better accuracy and real-time performance than PF algorithm.

Target tracking in a wireless sensor network (WSN) has become a relatively standard problem. The WSN typically consists of a collection of sensor nodes, which acquire physical data related to the target dynamics, and a fusion center (FC) where the available data are processed together to sequentially estimate the target state (its instantaneous location and velocity). Very often, tracking algorithms are designed under the assumption that the network is synchronous, i.e., that the local clocks of the sensor nodes and the FC are perfectly aligned or, at least, that their offsets are known. In this paper, we consider an asynchronous WSN, in which the local clocks of the sensors are misaligned and the corresponding offsets are unknown, and aim at designing recursive algorithms for optimal (Bayesian) tracking. In particular, we propose sequential Monte Carlo (SMC) techniques that enable the approximation of the joint posterior probability distribution of the target state and the set of local clock offsets by means of a discrete probability measure with a random support. From this approximation, estimates of the target position and velocity, as well as of the clock offsets, can be readily derived. We illustrate the validity of the proposed approach and assess the performance of the resulting algorithms by means of computer simulations.

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Maneuvering target tracking in wireless sensor networks (WSNs) has been a prominent research topic over the past twenty years. In many situations, WSNs are vulnerable to natural or man-made interference, resulting in uncertainty problems. In this paper, we propose an information-quality-based sensor selection method with estimation feedback for maneuvering target tracking in uncertain WSNs. First, based on the Generalized Pseudo-Bayesian estimator of first order (GPB1) and the unscented information filter (UIF), a GPB1-UIF algorithm with estimation feedback is presented. Inspired by the theory of information quality, an information-quality metric framework is proposed for sensor selection in uncertain WSNs. And the information-theoretic selection metrics are redesigned based on consistency measuring operator. Moreover, without knowing the number of selected sensors a priori, a multi-objective optimization problem is considered to determine the final selection strategy. Simulation results demonstrate that the proposed approach outperform the previous sensor selection methods.

Firefly algorithm (FA) based particle filter method for visual tracking

Simultaneous localization and tracking (SLAT) in wireless sensor networks (WSNs) involves tracking the mobile target while calibrating the nearby sensor node locations. In practice, localization error propagation (EP) phenomenon will arise, due to the existence of the latest tracking error, target mobility, measurement error, and reference node location errors. In this case, the SLAT performance limits are crucial for the SLAT algorithm design and WSN deployment, and the study of localization EP principle is desirable. In this paper, we focus on the EP issues for the received signal strength-based SLAT scheme, where the measurement accuracy is assumed to be spatial-temporal-domain doubly random due to the target mobility, environment dynamics, and different surroundings at different reference nodes. First, the Cramer-Rao lower bound (CRLB) is derived to unveil both the target tracking EP and the node location calibration EP. In both cases, the EP principles turn out to be in a consistent form of the Ohm's Law in circuit theory. Second, the asymptotic CRLB analysis is then presented to reveal that both EP principles scale with the inverse of sensor node density. Meanwhile, it is shown that, the tracking and calibration accuracy only depends on the expectation of the measurement precision. Third, the convergence conditions, the convergence properties, and the balance state of the target tracking EP and the location calibration EP are examined to shed light on the EP characteristics of the SLAT scheme Finally, numerical simulations are presented to corroborate the EP analysis.

Particle degeneracy and its poor ability to track saltatory states are two main problems when a particle filter is applied to fault prediction.The sequential importance re-sampling particle filter can abate the influence of particle degeneracy but will easily lead to another problem-sample impoverishment.The extended particle filter can resolve the problem of particle degeneracy to a certain extent but can not track the saltatory state.A strong tracking particle filter is put forward by introducing a strong tracking filter into a particle filter to resolve the above problems,in which the strong tracking filter is used to update particles and produce importance densities.As a result, the problems of particle degeneracy and sample impoverishment are ameliorated,and the tracking ability is improved. Simulation results demonstrate that the strong tracking particle filter is feasible and system fault can be predicted in time and correctly.

A particle filter is a sequential Monte Carlo method that is superior in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. Due to its nature, a particle filter has been regarded as an appropriate algorithm for localization. However, conventional problems, such as sample impoverishment and degeneracy problem, have not been perfectly solved yet. To solve these problems in mobile wireless environment, we propose an enhanced localization scheme, called Gaussian kernel density estimation-based particle filtering (GKPF), which calculates the target distribution (for location estimation) based on nonparametric technique. In order to estimate the target distribution, the GKPF algorithm creates both unimodal and multimodal distributions based on particle representations, and it calculates a pdf for each distribution with Gaussian kernel-density estimation. Simulation study indicates that the proposed GKPE scheme can accurately estimate the location in mobile wireless environment.

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.

Over the last few years, target tracking in wireless sensor networks has become a topic of particular interest. This paper presents a tracking system intended for deployment in distributed wireless sensor networks. The approach is inspired from the concept of deploying sensor nodes in an ad-hoc manner and based on the aggregate amount of information they provide, perform tracking of mobile objects. Static sensor nodes, with known locations, act as anchor nodes providing range-only measurements at each time step. The proposed system is modeled using a nonlinear state-space model which reflects a real world tracking scenario. The batch of range measurements made available at each sampling step, is used to estimate the target's desired kinematic properties. In order to infer the state of the target at each time step, a particle filter algorithm has been designed to approximate the required posterior distribution of the state vector. The system's operation was simulated and execution examples demonstrate the algorithm's accuracy as well as the ability to effectively cope with manoeuvring targets.

Consider a scenario where a subset of sensors has been selected for target tracking in an energy-constrained wireless sensor network. They communicate their quantized state measurements over noisy Rayleigh fading channels to a fusion center, where centralized estimation of target state is performed. In this context, we consider the problem of determining optimum quantization bits and transmission power per bit for all sensors such that both resource utilization and overall estimation error at the fusion center are minimized. Unlike prior efforts, we incorporate the operating state (characterized by the amount of residual battery power) of the sensors in the optimization framework. Analytical solutions for optimum power and number of quantization bits are derived and validated with numerical examples through simulations. We study the effect of channel quality, local measurement noise, and operating states of the sensors on their optimum choice for quantization bits and transmit power per bit.

Bandwidth reservation is a key resource management strategy for target tracking in Wireless Sensor Networks (WSNs). To keep the losing probability of targets coming in a specific region below an optimal value, we propose a QoS-guaranteed bandwidth reservation scheme in this paper. To detect targets with high probability, we reserve enough available bandwidth for potential incoming targets and take quality of link into account at the same time. Furthermore, we regulate the rate for every detected target in case we lose track of it because of lack of bandwidth. Performance analysis and simulation results show that our scheme can provide guarantee for keeping trace of both incoming targets and detected targets.

In this paper, 3D target tracking in wireless sensor network (WSN) is considered. Unlike other published work, this paper addresses the problem of estimating locations of different targets in 3D environment. Each sensor makes T observations to get local decisions. These decisions are modulated using on-off keying (OOK) and sent to a central node called fusion center. The fusion center detects all decisions sent from all sensors to process them in order to estimate the targets locations using maximum likelihood estimator. This approach generalizes the existing work in the literature about a single target localization if we considered that the number of targets equal one. The corresponding Cramer-Rao lower bound (CRLB) has been also derived. Furthermore, simulation results are provided and discussed. 

This paper proposes a light-weight sensing node selection scheme, which incorporates both information utility measure and energy consumption measure for distributive target tracking in wireless sensor networks. By adopting this scheme, the target tracking algorithm could maintain lower energy consumption while still achieve desirable tracking accuracy. Simulation results are provided in this paper.

Monte Carlo Methods for Node Self-Localization and Nonlinear Target Tracking in Wireless Sensor Networks