General direction-of-arrival tracking with acoustic nodes

In this paper, we propose a particle filter acoustic direction-of-arrival (DOA) tracker to track multiple maneuvering targets using a state space approach. The particle filter determines its state vector using a batch of DOA estimates. The filter likelihood treats the observations as an image, using template models derived from the state update equation, and also incorporates the possibility of missing data as well as spurious DOA observations. The particle filter handles multiple targets, using a partitioned state-vector approach. The particle filter solution is compared with three other methods: the extended Kalman filter, Laplacian filter, and another particle filter that uses the acoustic microphone outputs directly. We discuss the advantages and disadvantages of these methods for our problem. In addition, we also demonstrate an autonomous system for multiple target DOA tracking with automatic target initialization and deletion. The initialization system uses a track-before-detect approach and employs the matching pursuit idea to initialize multiple targets. Computer simulations are presented to show the performances of the algorithms

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

An acoustic multiple target tracker

We propose a particle filter acoustic tracker to track multiple maneuvering targets using a state space formulation with a locally linear motion model. The observations are a batch of direction-of-arrival (DOA) estimates at various frequencies. The data likelihood incorporates the possibility of missing data as well as Spurious DOA observations. By imposing smoothness constraints on the target motion, the particle filter is able to avoid data association problems. To make the filter computationally efficient, a proposal strategy based on approximating the full posterior with Newton's method is employed. Computer simulations show the algorithm's performance

Acoustic node calibration using helicopter sounds and Monte Carlo markov chain methods

A Monte-Carlo method is used to calibrate a randomly placed sensor node using helicopter sounds. The calibration is based on using the GPS information from the helicopter and the estimated DOA's at the node. The related Cramer-Rao lower bound is derived and the effects of the GPS errors on the position estimates are derived. Issues related to the processing of the field data, e.g., time synchronization and data nonstationarity are discussed. The effects of the GPS errors are shown to be negligible under certain conditions. Finally, the results of the calibration on field data are given

Estimating target state distributions in a distributed sensor network using a Monte-Carlo approach

Distributed processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in sensor networks. In this paper, we address the issue of determining a initial probability distribution of multiple target states in a distributed manner to initialize distributed trackers. Our approach is based on Monte-Carlo methods, where the state distributions are represented as a discrete set of weighted particles. The target state vector is the target positions and velocities in the 2D plane. Our approach can determine the state vector distribution even if the individual sensors are not capable of observing it. The only condition is that the network as a whole can observe the state vector. A robust weighting strategy is formulated to account for mis-detections and clutter. To demonstate the effectiveness of the algorithm, we use direction-of-arrival nodes and range-doppler nodes

Multi target direction-of-arrival tracking using road priors

In this paper, we present a multi target particle filter DOA tracker that can incorporate road prior information at a single array node. The filter uses a batch of DON's to determine the state vector, based on an image template matching idea. The filter likelihood is derived with the joint probability density association principles so that no DOA measurement is associated to more than one target. The filter state update has the target DOA, the target velocity over range ratio, and the target heading parameters. We present two approaches for incorporating the road information. In the first approach, the road prior is injected at the weighting stage of the tracker, where a raised mixture Gaussian distribution, derived from the road headings at the target DOA, constraints the particles. The second approach is based on modifying the state update function with a compound model, where a mixture of the constant velocity model and the road information is used. In this case, the filter uses an online EM algorithm to update the state vector along with the mixture components. Computer simulations demonstrate the performance of the approaches

Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks

Decentralized processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in smart sensor networks since they provide the ability to scale, reduce vulnerability, reduce communication and share processing responsibilities among individual nodes. Sharing the processing responsibilities allows parallel processing of raw data at the individual nodes. However, this introduces other difficulties in multi-modal smart sensor networks, such as non- observability of the target state at any individual node and various delays such as varying processing delays, communication delays and signal propagation delays for the different modalities. In this paper, we provide a novel algorithm to determine the initial probability distribution of multiple target states in a decentralized manner. The targets state vector consists of the target positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors alone are not capable of observing it. Our approach can also compensate for varying delays among the assorted modalities. The resulting distribution can be used to initialize various tracking algorithms. Our approach is based on Monte-Carlo methods, where the state distributions are represented as a weighted set of discrete state realizations. A robust weighting strategy is formulated to account for missed detections, clutter and estimation delays. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-doppler nodes

Acoustic node calibration using moving sources

Acoustic nodes, each containing an array of microphones, can track targets in x-y space from their received acoustic signals, if the node positions and orientations are known exactly. However, it is not always possible to deploy the nodes precisely, so a calibration phase is needed to estimate the position and the orientation of each node before doing any tracking or localization. An acoustic node can be calibrated from sources of opportunity such as beacons or a moving source. In this paper, we derive and compare several calibration methods for the case where the node can hear a moving source whose position can be reported back to the node. Since calibration from a moving source is, in effect, the dual of a tracking problem, methods derived for acoustic target trackers are used to obtain robust and high resolution acoustic calibration processes. For example, two direction-of-arrival-based calibration methods can be formulated based on combining angle estimates, geometry, and the motion dynamics of the moving source. In addition, a maximum-likelihood (ML) solution is presented using a narrow-band acoustic observation model, along with a Newton-based search algorithm that speeds up the calculation the likelihood surface. The Cramer-Rao lower bound on the node position estimates is also derived to show that the effect of position errors for the moving source on the estimated node position is much less severe than the variance in angle estimates from the microphone array. The performance of the calibration algorithms is demonstrated on synthetic and field data

Target tracking using a joint acoustic video system

In this paper, we present a particle filter that exploits multi modal information for robust target tracking. We demonstrate a Bayesian framework for combining acoustic and video information using a state space approach. A proposal strategy for joint acoustic and video state-space tracking using particle filters is given by carefully placing the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint filter posterior estimate decreases the worst case divergence of the individual modalities. Hence, the joint tracking filter is robust against video and acoustic occlusions. We also introduce a time-delay variable to the joint state space to handle the acoustic-video data synchronization issue, caused by acoustic propagation delay. Computer simulations are presented with field and synthetic data to demonstrate the filter’s performance