Two-layer particle filter for multiple target detection and tracking

This paper deals with the detection and tracking of an unknown number of targets using a Bayesian hierarchical model with target labels. To approximate the posterior probability density function, we develop a two-layer particle filter. One deals with track initiation, and the other with track maintenance. In addition, the parallel partition method is proposed to sample the states of the surviving targets

Neural Sensor Fusion for Spatial Visualization on a Mobile Robot

An ARTMAP neural network is used to integrate visual information and ultrasonic sensory information on a B 14 mobile robot. Training samples for the neural network are acquired without human intervention. Sensory snapshots are retrospectively associated with the distance to the wall, provided by on~ board odomctry as the robot travels in a straight line. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. The neural network effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.Office of Naval Research and Naval Research Laboratory (00014-96-1-0772, 00014-95-1-0409, 00014-95-0657

SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field

Energy Efficient Clustering and Routing in Mobile Wireless Sensor Network

A critical need in Mobile Wireless Sensor Network (MWSN) is to achieve energy efficiency during routing as the sensor nodes have scarce energy resource. The nodes' mobility in MWSN poses a challenge to design an energy efficient routing protocol. Clustering helps to achieve energy efficiency by reducing the organization complexity overhead of the network which is proportional to the number of nodes in the network. This paper proposes a novel hybrid multipath routing algorithm with an efficient clustering technique. A node is selected as cluster head if it has high surplus energy, better transmission range and least mobility. The Energy Aware (EA) selection mechanism and the Maximal Nodal Surplus Energy estimation technique incorporated in this algorithm improves the energy performance during routing. Simulation results can show that the proposed clustering and routing algorithm can scale well in dynamic and energy deficient mobile sensor network.Comment: 9 pages, 4 figure

Hybrid Poisson and multi-Bernoulli filters

The probability hypothesis density (PHD) and multi-target multi-Bernoulli (MeMBer) filters are two leading algorithms that have emerged from random finite sets (RFS). In this paper we study a method which combines these two approaches. Our work is motivated by a sister paper, which proves that the full Bayes RFS filter naturally incorporates a Poisson component representing targets that have never been detected, and a linear combination of multi-Bernoulli components representing targets under track. Here we demonstrate the benefit (in speed of track initiation) that maintenance of a Poisson component of undetected targets provides. Subsequently, we propose a method of recycling, which projects Bernoulli components with a low probability of existence onto the Poisson component (as opposed to deleting them). We show that this allows us to achieve similar tracking performance using a fraction of the number of Bernoulli components (i.e., tracks).Comment: Submitted to 15th International Conference on Information Fusion (2012