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

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

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

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

Digital Signal Processing

Contains reports on one research project.National Science Foundation (Grant ENG71-02319-AO2

Optimal experiments with seismic sensors

In this paper, we consider the problem of detecting and locating buried land mines and subsurface objects by using seismic waves. We demonstrate an adaptive seismic system that maneuvers an array of receivers, according to an optimal positioning algorithm based on the theory of optimal experiments, to minimize the number of distinct measurements to localize the mine. The adaptive localization algorithm is tested using numerical model data as well as laboratory measurements performed in a facility at Georgia Tech. It is envisioned that the future systems should be able to incorporate this new method into portable mobile mine-location systems