Distributed multi-target tracking using joint probabilistic data association and average consensus filter

In this paper, a distributed multi-target tracking (MTT) algorithm suitable for implementation in wireless sensor networks is proposed. For this purpose, the Monte Carlo (MC) implementation of joint probabilistic data-association filter (JPDAF) is applied to the well-known problem of multi-target tracking in a cluttered area. Also, to make the tracking algorithm scalable and usable for sensor networks of many nodes, the distributed expectation maximization algorithm is exploited via the average consensus filter, in order to diffuse the nodes’ information over the whole network. The proposed tracking system is robust and capable of modeling any state space with nonlinear and non-Gaussian models for target dynamics and measurement likelihood, since it uses the particle-filtering methods to extract samples from the desired distributions. To encounter the data-association problem that arises due to the unlabeled measurements in the presence of clutter, the well-known JPDAF algorithm is used. Furthermore, some simplifications and modifications are made to MC–JPDAF algorithm in order to reduce the computation complexity of the tracking system and make it suitable for low-energy sensor networks. Finally, the simulations of tracking tasks for a sample network are given.     

Sensor networks: evolution, opportunities, and challenges

Wireless microsensor networks have been identified as one of the most important technologies for the 21st century. This paper traces the history of research in sensor networks over the past three decades, including two important programs of the Defense Advanced Research Projects Agency (DARPA) spanning this period: the Distributed Sensor Networks (DSN) and the Sensor Information Technology (SensIT) programs. Technology trends that impact the development of sensor networks are reviewed, and new applications such as infrastructure security, habitat monitoring, and traffic control are presented. Technical challenges in sensor network development include network discovery, control and routing, collaborative signal and information processing, tasking and querying, and security. The paper concludes by presenting some recent research results in sensor network algorithms, including localized algorithms and directed diffusion, distributed tracking in wireless ad hoc networks, and distributed classification using local agents.     

Efficient in-network moving object tracking in wireless sensor networks

The rapid progress of wireless communication and embedded microsensing MEMS technologies has made wireless sensor networks possible. In light of storage in sensors, a sensor network can be considered as a distributed database, in which one can conduct in-network data processing. An important issue of wireless sensor networks is object tracking, which typically involves two basic operations: update and query. This issue has been intensively studied in other areas, such as cellular networks. However, the in-network processing characteristic of sensor networks has posed new challenges to this issue. In this paper, we develop several tree structures for in-network object tracking which take the physical topology of the sensor network into consideration. The optimization process has two stages. The first stage tries to reduce the location update cost based on a deviation-avoidance principle and a highest-weight-first principle. The second stage further adjusts the tree obtained in the first stage to reduce the query cost. The way we model this problem allows us to analytically formulate the cost of object tracking given the update and query rates of objects. Extensive simulations are conducted, which show a significant improvement over existing solutions.     

视野有限的传感器网络中分布式多目标跟踪

在传感器网络的多目标跟踪研究中,大多数现有的跟踪算法通常设定网络中所有节点具有相同的视野,即所有节点都能够得到目标的测量,但在实际中,节点的感测范围通常是有限的。针对这一问题,本文提出了一种能够在感测范围有限的多传感器网络中实现多目标跟踪的分布式概率假设密度滤波算法,该算法通过融合传感器网络视野范围内的后验概率假设密度粒子集来克服传感器节点感测范围的局限。仿真结果表明,提出的算法可以在感测范围有限的情况下实现多目标状态和数目的有效跟踪,同时可以在一定程度上抑制杂波,具有较好的跟踪稳定性。      

Distributed Active Sensor Scheduling for Target Tracking in Ultrasonic Sensor Networks

Active ultrasonic sensors for target tracking application may suffer from inter-sensor-interference if these highly dense deployed sensors are not scheduled, which can degrade the tracking performance. In this paper, we propose a dynamic distributed sensor scheduling (DSS) scheme, where the tasking sensor is elected spontaneously from the sensors with pending sensing tasks via random competition based on Carrier Sense Multiple Access (CSMA). The channel will be released immediately when sensing task is completed. Both simulation results and testbed experiment demonstrate the effectiveness of DSS scheme for large scale sensor networks in terms of system scalability and high tracking performance.     

Distributed Mobility Management for Target Tracking in Mobile Sensor Networks

Mobility management is a major challenge in mobile ad hoc networks (MANETs) due in part to the dynamically changing network topologies. For mobile sensor networks that are deployed for surveillance applications, it is important to use a mobility management scheme that can empower nodes to make better decisions regarding their positions such that strategic tasks such as target tracking can benefit from node movement. In this paper, we describe a distributed mobility management scheme for mobile sensor networks. The proposed scheme considers node movement decisions as part of a distributed optimization problem which integrates mobility-enhanced improvement in the quality of target tracking data with the associated negative consequences of increased energy consumption due to locomotion, potential loss of network connectivity, and loss of sensing coverage.     

Distributed target classification and tracking in sensor networks

The highly distributed infrastructure provided by sensor networks supports fundamentally new ways of designing surveillance systems. In this paper, we discuss sensor networks for target classification and tracking. Our formulation is anchored on location-aware data routing to conserve system resources, such as energy and bandwidth. Distributed classification algorithms exploit signals from multiple nodes in several modalities and rely on prior statistical information about target classes. Associating data to tracks becomes simpler in a distributed environment, at the cost of global consistency. It may be possible to filter clutter from the system by embedding higher level reasoning in the distributed system. Results and insights from a recent field test at 29 Palms Marine Training Center are provided to highlight challenges in sensor networks.     

基于动态分簇路由优化和分布式粒子滤波的传感器网络目标跟踪方法

针对无线传感器网络中节点通信能力及能量有限的情况,该文提出基于动态分簇路由优化和分布式粒子滤波的传感器网络目标跟踪方法。该方法以动态分簇的方式将监测区域内随机部署的传感器节点划分为若干个簇,并对簇内成员节点与簇首节点之间、簇首节点与基站之间的通信路由进行优化,确保网络能耗的均衡分布,在此基础上,被激活的簇内成员节点并行地执行分布式粒子滤波算法实现目标跟踪。仿真结果表明,该方法能有效地降低传感器网络中节点的总能耗,能在实现跟踪的同时保证目标跟踪的精度。     

分布式无线传感网络的协作目标跟踪策略

基于图像序列的目标跟踪是目标跟踪的重要研究问题之一.由于受图像解析度和跟踪范围限制,单视角跟踪准确性和鲁棒性不足.本文提出了分布式无线传感网络测量环境下的多视角协作融合跟踪方法,并引入了渐进分布式数据融合,采用基于能耗参数和信息有效性参数的综合优化函数动态选择融合节点,规划融合过程,平衡融合精度与网络能耗.通过目标跟踪准确性、网络能耗及传输延时对比实验表明:基于渐进分布式数据融合的协作信号处理方法提高了分布式多视角跟踪的准确性与实时性,减少了网络拥塞,降低了通讯能耗及延时.     

Non-stationary Channel Estimation with Diffusion Adaptation Strategies Over Distributed Networks

In this paper we address non-stationary channel estimation problem with diffusion least mean square algorithm in distributed adaptive wireless sensor networks. Here we estimate channel coefficients or taps that are produced with Rayleigh fading models. All detailed explanations regarding to this fading channel type are presented and it is explained that how we can extend channel estimation with sensor networks to other newly presented channel types. We use the tracking performance analysis of diffusion cooperation over adaptive sensor networks to investigate the reliability of used algorithms and show the link between channel estimation problem and tracking a time varying entity. Theoretical analyzes are performed and the results are compared with simulation performance diagrams. It is proven that there is a reasonable match between these two outcomes. We present our results with the mean square deviation criteria.     

Collaborative signal and information processing: an information-directed approach

This paper describes information-based approaches to processing and organizing spatially distributed, multimodal sensor data in a sensor network. Energy-constrained networked sensing systems must rely on collaborative signal and information processing (CSIP) to dynamically allocate resources, maintain multiple sensing foci, and attend to new stimuli of interest, all based on task requirements and resource constraints. Target tracking is an essential capability for sensor networks and is used as a canonical problem for studying information organization problems in CSIP. After formulating a CSIP tracking problem in a distributed constrained optimization framework, the paper describes information-driven sensor query and other techniques for tracking individual targets as well as combinatorial tracking problems such as counting targets. Results from simulations and experimental implementations have demonstrated that these information-based approaches are scalable and make efficient use of scarce sensing and communication resources.     

Detection, classification, and tracking of targets

Networks of small, densely distributed wireless sensor nodes are being envisioned and developed for a variety of applications involving monitoring and the physical world in a tetherless fashion. Typically, each individual node can sense in multiple modalities but has limited communication and computation capabilities. Many challenges must be overcome before the concept of sensor networks In particular, there are two critical problems underlying successful operation of sensor networks: (1) efficient methods for exchanging information between the nodes and (2) collaborative signal processing (CSP) between the nodes to gather useful information about the physical world. This article describes the key ideas behind the CSP algorithms for distributed sensor networks being developed at the University of Wisconsin (UW). We also describe the basic ideas on how the CSP algorithms interface with the networking/routing algorithms being developed at Wisconsin (UW-API). We motivate the framework via the problem of detecting and tracking a single maneuvering target. This example illustrates the essential ideas behind the integration between UW-API and UW-CSP algorithms and also highlights the key aspects of detection and localization algorithms. We then build on these ideas to present our approach to tracking multiple targets that necessarily requires classification techniques becomes a reality     

Distributed Group Management in Sensor Networks: Algorithms and Applications to Localization and Tracking

The tradeoff between performance and scalability is a fundamental issue in distributed sensor networks. In this paper, we propose a novel scheme to efficiently organize and utilize network resources for target localization. Motivated by the essential role of geographic proximity in sensing, sensors are organized into geographically local collaborative groups. In a target tracking context, we present a dynamic group management method to initiate and maintain multiple tracks in a distributed manner. Collaborative groups are formed, each responsible for tracking a single target. The sensor nodes within a group coordinate their behavior using geographically-limited message passing. Mechanisms such as these for managing local collaborations are essential building blocks for scalable sensor network applications.     

Optimal tracking interval for predictive tracking in wireless sensor network

An important application of wireless sensor networks is tracking moving objects. Prediction-based techniques have been proposed to reduce the power consumption in wireless sensor networks by limiting the sensor active time. This paper proposes a quantitative method to optimize the power efficiency by analyzing the effect of prediction on the energy consumption in such networks. To our best knowledge, our efforts are the first attempt made to calculate the optimal tracking interval for a given predictive tracking algorithm. Based on this method, the lifetime and power efficiency of a sensor network can be effectively improved.     

Low computation and low latency algorithms for distributed sensor network initialization

In this paper, we show how an underlying system’s state vector distribution can be determined in a distributed heterogeneous sensor network with reduced subspace observability at the individual nodes. The presented algorithm can generate the initial state vector distribution for networks with a variety of sensor types as long as the collective set of measurements from all the sensors provides full state observability. Hence the network, as a whole, can be capable of observing the target state vector even if the individual nodes are not capable of observing it locally. Initialization is accomplished through a novel distributed implementation of the particle filter that involves serial particle proposal and weighting strategies that can be accomplished without sharing raw data between individual nodes. If multiple events of interest occur, their individual states can be initialized simultaneously without requiring explicit data association across nodes. The resulting distributions can be used to initialize a variety of distributed joint tracking algorithms. We present two variants of our initialization algorithm: a low complexity implementation and a low latency implementation. To demonstrate the effectiveness of our algorithms we provide simulation results for initializing the states of multiple maneuvering targets in smart sensor networks consisting of acoustic and radar sensors. Prepared through collaborative participation in the Advanced Sensors Consortium sponsored by the US Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-02-0008.     

用无线传感器网络探测跟踪目标

与传统的探测跟踪方法相比,无线传感器网络以其良好的特性弥补了传统跟踪方法的不足。文章介绍了无线传感器网络的体系结构,探讨了无线传感器网络探测跟踪目标的策略和方法,最后提出了用无线传感器网络跟踪目标需要考虑的问题。     

一种高效的分布式多传感器多目标跟踪算法

现有的多传感器多目标跟踪算法大都基于马尔科夫-贝叶斯模型,需要诸如目标运动、杂波、传感器检测概率等先验信息,但是在恶劣的环境中,这些先验信息不准确并导致目标跟踪精度下降.为了解决该情况下的多目标跟踪问题,我们提出了一个高效的分布式多目标跟踪算法,该算法通过泛洪(Flooding)共识算法在分布式网络的传感器之间迭代的传...     

A Novel Distributed Sensor Fusion Algorithm for RSSI-Based Location Estimation Using the Unscented Kalman Filter

We address the Bayesian sensor fusion approach for distributed location estimation in the wireless sensor network. Assume each sensor transmits local calculation of target position to a fusion center, which then generates under a Bayesian framework the final estimated trajectory. We study received signal strength indication-based approach using the unscented Kalman filter for each sensor to compute local estimation, and propose a novel distributed algorithm which combines the soft outputs sent from selected sensors and computes the approximated Bayesian estimates to the true position. Simulation results demonstrate that the proposed soft combining method can achieve similar tracking performance as the centralized data fusion approach. The computational cost of the proposed algorithm is less than the centralized method especially in large scale sensor networks. In addition, it is straightforward to incorporate the proposed soft combining strategy with other Bayesian filters for the general purpose of data fusion.

     

Dynamic proxy tree-based data dissemination schemes for wireless sensor networks

In wireless sensor networks, efficiently disseminating data from a dynamic source to multiple mobile sinks is important for the applications such as mobile target detection and tracking. The tree-based multicasting scheme can be used. However, because of the short communication range of each sensor node and the frequent movement of sources and sinks, a sink may fail to receive data due to broken paths, and the tree should be frequently reconfigured to reconnect sources and sinks. To address the problem, we propose a dynamic proxy tree-based framework in this paper. A big challenge in implementing the framework is how to efficiently reconfigure the proxy tree as sources and sinks change. We model the problem as on-line constructing a minimum Steiner tree in an Euclidean plane, and propose centralized schemes to solve it. Considering the strict energy constraints in wireless sensor networks, we further propose two distributed on-line schemes, the shortest path-based (SP) scheme and the spanning range-based (SR) scheme. Extensive simulations are conducted to evaluate the schemes. The results show that the distributed schemes have similar performance as the centralized ones, and among the distributed schemes, the SR scheme outperforms the SP scheme.     

Distributed object tracking using moving trajectories in wireless sensor networks

Most recent research on object tracking sensor networks has focused on collecting all data from the sensor network into the sink, which delivers the predicted locations to the corresponding nodes in order to accurately predict object movement. The communication cost of this centralized scenario is higher than that of a distributed method. Centralized data collection affects the freshness of the data and increases latency in movement trajectory prediction. In addition, due to the large amount of packets being sent and received, sensor node energy is quickly exhausted. Although this data collection method might result in higher accuracy for prediction, the sensor network lifetime is not reduced. In this paper, a distributed object tracking method is proposed using the network structure of convex polygons, called faces. The nodes in the faces cooperate to find the trajectories of an object and then these trajectories are used to predict the objects’ movement. The proposed method, based on trajectory tree construction, can reduce both the storage space of collected trajectories and the time spent on trajectory prediction analysis. Simulations show that the proposed method can reduce the energy consumption of the nodes and make prediction of nodes moving direction accurately than the existing approaches.