Completely Distributed Particle Filters for Target Tracking in Sensor Networks

We consider the problem of target tracking in a wireless sensor network (WSN) that consists of randomly distributed range-only sensors. Quantized measurements are usually adopted in such a network to attack the problem of limited power supply and communication bandwidth. Assuming that local sensor noises are mutually independent, we derive the posterior Cramer-Rao lower bound (CRLB) on the mean squared error (MSE) of target tracking in WSNs with quantized range-only measurements. Recursion of posterior CRLB on tracking based on both constant velocity (CV) and constant acceleration (CA) model for target dynamics and a general range-only measuring model for local sensors are obtained. Due to the analytical difficulties, particle filter is applied to approximate the theoretical bounds. To illustrate the posterior CRLB, an example on tracking a target with noisy circular trajectories is given.

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.

We study the problem of sensor-scheduling for target tracking to determine which sensors to activate over time to trade off tracking performance with sensor usage costs. We approach this problem by formulating it as a partially observable Markov decision process (POMDP), and develop a Monte Carlo solution method using a combination of particle filtering for belief-state estimation and sampling based Q-value approximation for lookahead. To evaluate the effectiveness of our approach, we consider a simple sensor scheduling problem involving multiple sensors for tracking a single target.

무선 센서 네트워크 환경에서 이동 객체 추적 기법은 환경 모니터링이나 군사 지역에서 적의 이동을 추적하는 실제 응용을 위한 핵심적인 기반 기술이다. 기존 연구에서는 저밀도를 갖는 실제 센서 네트워크 환경에 의해 발생되는 감지 공백 영역을 고려하지 않았다. 따라서 많은 이동 객체 추적 실패가 발생하여 에너지 소모가 증가하였다. 이러한 문제를 해결하기 위해 본 논문에서는 저밀도 환경과 감지 공백 영역을 고려한 이동 객체 추적 기법을 제안한다. 제안하는 기법은 다항 회귀 분석을 이용해 객체의 경로를 예측하여 최소한의 센서 노드를 활성화시킨다. 또한 이동 객체 추적 실패가 발생할 경우 감지 공백 영역의 경계 노드만을 활성화 시키는 객체 추적 복구 기법을 수행한다. 이를 통해, 제안하는 기법은 에너지 소모량을 줄이고 감지 공백 영역 안에서도 높은 예측 정확도를 보장한다. 제안하는 기법이 기존 기법에 비해 이동 객체 추적에 소모되는 에너지를 평균 약 47% 감소시켰고, 센서 노드가 낮은 밀도로 배치된 상황에서 발생하는 감지 공백 영역에서도 평균 약 91%의 예측 정확도를 보였다. In wireless sensor networks, a moving object tracking scheme is one of core technologies for real applications such as environment monitering and enemy moving tracking in military areas. However, no works have been carried out on processing the failure of object tracking in sparse sensor networks with holes. Therefore, the energy consumption in the existing schemes significantly increases due to plenty of failures of moving object tracking. To overcome this problem, we propose a novel moving object tracking scheme based on polynomial regression prediction in sparse sensor networks. The proposed scheme activates the minimum sensor nodes by predicting the trajectory of an object based on polynomial regression analysis. Moreover, in the case of the failure of moving object tracking, it just activates only the boundary nodes of a hole for failure recovery. By doing so, the proposed scheme reduces the energy consumption and ensures the high accuracy for object tracking in the sensor network with holes. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme reduces about 47% energy consumption for object tracking over the existing scheme and achieves about 91% accuracy of object tracking even in sensor networks with holes.

Due to recent advances in computation power, particle filtering has become a very promising tool for nonlinear non- Gaussian tracking applications. In this paper, a new framework for target tracking in a wireless sensor network using particle filtering is proposed. Under this framework, the imperfect nature of the communication channels between sensors and the fusion center is taken into account, and the channel information as well as inherent limitations of wireless sensors are incorporated in the tracking algorithm based on particle filters. Simulation results are presented to demonstrate the superior performance of the proposed algorithm.

A distributed two-tier near optimal algorithm is proposed for target tracking in sensor networks. Tier one is a multiple hypothesis tracking (MHT) algorithm where the Viterbi algorithm is used. In this tier, only binary data is used to obtain a rough region around the target. Tier two improves the accuracy of the MHT decision by localized maximum likelihood. This reduces the computational complexity and the communication costs between sensors over the global maximum likelihood approach. It also results in higher sensor power efficiency, hence longer service time of the tracking network. This two-tier system is a distributed near optimal tracking algorithm. The localized maximum likelihood tracking can tolerate errors made by the Viterbi algorithm in tier one, hence the overall algorithm is robust.

In this paper, we present a hormone based tracking strategy called Hormone based Activation (HA) for mobile target tracking in wireless sensor networks inspired by the bio-endocrine system. Hormone messages are used in sensor networks to coordinate sensor nodes for mobile target tracking in a self-adaptive way. Three kinds of hormones are introduced into sensor network for mobile target tracking: Base Hormone (BH),Wakeup Hormone (WH) and Sleep Hormone (SH).Based on different hormone messages, sensor nodes can choose to be asleep or awake in order to get good tracking results with less energy consumed. Comparative experiment shows that our strategy needs fewer nodes to wake up and has a much higher usage ratio of sensor nodes than other tracking strategies.

Improved Particle Filter for Target Tracking

In this paper, we consider the problem of sensor selection in wireless sensor networks for target tracking, subject to limited energy consumption and a given tracking accuracy. A cost function is derived for this purpose based on the geometrical dilution of precision for the power measurement sensors. Then, a new adaptive method is proposed, in which the number of active sensors is adaptively determined based on the energy consumption and tracking error constraints, and the best topology of the active set is selected based on the closest sensor with replacement (CSWR) method. Simulation results show that the proposed sensor selection problem is effective in terms of balancing the tradeoff between energy consumption and tracking accuracy. Also, the CSWR algorithm is near optimal and its computational burden is comparable with the suboptimum algorithms.

One of the most important challenges in target tracking is the modeling of correlated and non-Gaussian disturbance. In this paper, the problem of target tracking by the means of particle filtering in environments with highly correlated sensors, is speculated. The goal is to prepare an accurate model of dependency structure in multivariate observation likelihood function, with non-Gaussian marginals to achieve a new algorithm in tracking problems. The main novelty of our method, termed as Copula-based Sequential Importance Resampling particle filter (CSIR-PF), is the application of the copula theory which is a powerful tool in correlation modeling in statistical theory. The precise model obtained by copulas, makes a great improvement in particles' weighting. The performance of our proposed method is depicted through simulations of problems with highly correlated sensors. Results clearly indicate the acceptable performance of CSIR-PF.

Energy-efficient real-time object tracking in multi-level sensor networks by mining and predicting movement patterns

Sampling is one of the most common and repeated tasks in a target tracking sensor network. However, tuning the sampling rate parameter can be a challenging issue considering all the sensor network restrictions. In this paper, we propose two adaptive sampling algorithms in a target tracking sensor network while considering a multi-objective fitness function. The restrictions used as objective functions are energy consumption and prediction error which provide a direct feedback to the sampling rate adaptation algorithms. We support our proposed methods with well structured experimental evaluations.

Successful power management in a wireless sensor network requires optimization of the protocols which affect energy-consumption on each node and the aggregate effects across the larger network. System optimization for a given deployment scenario requires an analysis and trade off of desired node and network features with their associated costs. The sleep protocol for an energy-efficient wireless sensor network for event detection, target classification, and target tracking developed at Sandia National Laboratories is presented. The dynamic source routing (DSR) algorithm is chosen to reduce network maintenance overhead, while providing a self-configuring and self-healing network architecture. A method for determining the optimal sleep time is developed and presented, providing reference data which spans several orders of magnitude. Message timing diagrams show, that a node in a five-node cluster, employing an optimal cyclic single-radio sleep protocol, consumes 3% more energy and incurs a 16-s increase latency than nodes employing the more complex dual-radio STEM protocol.

This paper presents a distributed optimal control approach for managing omnidirectional sensor networks deployed to cooperatively track moving targets in a region of interest. Several authors have shown that under proper assumptions, the performance of mobile sensors is a function of the sensor distribution. In particular, the probability of cooperative track detection, also known as track coverage, can be shown to be an integral function of a probability density function representing the macroscopic sensor network state. Thus, a mobile sensor network deployed to detect moving targets can be viewed as a multiscale dynamical system in which a time-varying probability density function can be identified as a restriction operator, and optimized subject to macroscopic dynamics represented by the advection equation. Simulation results show that the distributed control approach is capable of planning the motion of hundreds of cooperative sensors, such that their effectiveness is significantly increased compared to that of existing uniform, grid, random, and stochastic gradient methods.

Wireless sensor networks (WSNs), with self-organizing and high fault tolerant characteristics, have great advantages for target tracking. But the sensor nodes are battery powered and in most of the cases, it is impossible to charge or replace batteries. Therefore, energy efficiency to extende the l ife of the network has become an important design criterion for wireless sensor networks. At the same time, high communication speed is also an important issue in the target tracking application. In order to overcome these problems, we propose a novel fuzzy logic model which provides high communication speed to fulfil the tracking task of wireless sensor networks. The proposed algorithm is evaluated in MATLAB. The results show that the our algorithm performs better than the other algorithms in terms of data transmission rate and system lifetime.