A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks

Multisensor fusion and consensus filtering are two fascinating subjects in the research of sensor networks. In this survey, we will cover both classic results and recent advances developed in these two topics. First, we recall some important results in the development ofmultisensor fusion technology. Particularly, we pay great attention to the fusion with unknown correlations, which ubiquitously exist in most of distributed filtering problems. Next, we give a systematic review on several widely used consensus filtering approaches. Furthermore, some latest progress on multisensor fusion and consensus filtering is also presented. Finally, conclusions are drawn and several potential future research directions are outlined.the Royal Society of the UK, the National Natural Science Foundation of China under Grants 61329301, 61374039, 61304010, 11301118, and 61573246, the Hujiang Foundation of China under Grants C14002 and D15009, the Alexander von Humboldt Foundation of Germany, and the Innovation Fund Project for Graduate Student of Shanghai under Grant JWCXSL140

A SURVEY ON SENSOR NETWORKS

Abstract Wireless micro sensor 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

When Decision Meets Estimation: Theory and Applications

In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms

When Decision Meets Estimation: Theory and Applications

In many practical problems, both decision and estimation are involved. This dissertation intends to study the relationship between decision and estimation in these problems, so that more accurate inference methods can be developed. Hybrid estimation is an important formulation that deals with state estimation and model structure identification simultaneously. Multiple-model (MM) methods are the most widelyused tool for hybrid estimation. A novel approach to predict the Internet end-to-end delay using MM methods is proposed. Based on preliminary analysis of the collected end-to-end delay data, we propose an off-line model set design procedure using vector quantization (VQ) and short-term time series analysis so that MM methods can be applied to predict on-line measurement data. Experimental results show that the proposed MM predictor outperforms two widely used adaptive filters in terms of prediction accuracy and robustness. Although hybrid estimation can identify model structure, it mainly focuses on the estimation part. When decision and estimation are of (nearly) equal importance, a joint solution is preferred. By noticing the resemblance, a new Bayes risk is generalized from those of decision and estimation, respectively. Based on this generalized Bayes risk, a novel, integrated solution to decision and estimation is introduced. Our study tries to give a more systematic view on the joint decision and estimation (JDE) problem, which we believe the work in various fields, such as target tracking, communications, time series modeling, will benefit greatly from. We apply this integrated Bayes solution to joint target tracking and classification, a very important topic in target inference, with simplified measurement models. The results of this new approach are compared with two conventional strategies. At last, a surveillance testbed is being built for such purposes as algorithm development and performance evaluation. We try to use the testbed to bridge the gap between theory and practice. In the dissertation, an overview as well as the architecture of the testbed is given and one case study is presented. The testbed is capable to serve the tasks with decision and/or estimation aspects, and is helpful for the development of the JDE algorithms

MAC layer assisted localization in wireless environments with multiple sensors and multiple emitters

Extreme emitter density (EED) RF environments, defined as 10k-100k emitters within a footprint of less than 1 km squared, are becoming increasingly common with the proliferation of personal devices containing myriad communication standards (e.g. WLAN, Bluetooth, 4G, etc). Attendees at concerts, sporting events, and other such large-scale events desire to be connected at all times, creating tremendous spectrum management challenges, especially in unlicensed frequencies such as 2.4 GHz, 5 GHz, or 900 MHz Industrial, Scientific, and Medical (ISM) bands. In licensed bands, there are often critical communication systems such as two-way radios for emergency personnel which must be free from interference. Identification and localization of a non-conforming or interfering Emitter of Interest (EoI) is important for these critical systems. In this dissertation, research is conducted to improve localization for these EED RF environments by exploiting side information available at the Medium Access Control (MAC) layer. The primary contributions of this research are: (1) A testbed in Bobby Dodd football stadium consisting of three spatially distributed, time-synchronized RF Sensor Nodes (RFSN) collecting and archiving complex baseband samples for algorithm development and validation. (2) A modeling framework and analytical results on the benefits of exploiting the structure of the MAC layer for associating physical layer measurements, such as Time Difference of Arrivals (TDoA), to emitters. (3) A three stage localization algorithm exploiting time between packets and a constrained geometry to shrink the error ellipse of the emitter position estimate. The results are expected to improve localization accuracy in wireless environments when multiple sensors observe multiple emitters using a known communications protocol within a constrained geometry.Ph.D

Proceedings of the Fourth MIT/ONR Workshop on Distributed Information and Decision Systems Motivated by Command-Control-Communications (C3) Problems, June 15-June 26, 1981, San Diego, California

"OSP number 85552"--Cover.Library has v. 2 only.Includes bibliographies.Workshop suppported by the Office of Naval Research under contract ONR/N00014-77-C-0532edited by Michael Athans ... [et al.].v.1. Surveillance and target tracking--v.2. Systems architecture and evaluation--v.3. Communication, data bases & decision support--v.4. C3 theory

Data fusion for unsupervised video object detection, tracking and geo-positioning

In this work we describe a system and propose a novel algorithm for moving object detection and tracking based on video feed. Apart of many well-known algorithms, it performs detection in unsupervised style, using velocity criteria for the objects detection. The algorithm utilises data from a single camera and Inertial Measurement Unit (IMU) sensors and performs fusion of video and sensory data captured from the UAV. The algorithm includes object tracking and detection, augmented by object geographical co-ordinates estimation. The algorithm can be generalised for any particular video sensor and is not restricted to any specific applications. For object tracking, Bayesian filter scheme combined with approximate inference is utilised. Object localisation in real-world co-ordinates is based on the tracking results and IMU sensor measurements

Pattern-theoretic foundations of automatic target recognition in clutter

Issued as final reportAir Force Office of Scientific Research (U.S.