Space-time Coordinated Distributed Sensing Algorithms for Resource Efficient Narrowband Target Localization and Tracking

Distributed sensing has been used for enhancing signal to noise ratios for space-time localization and tracking of remote objects using phased array antennas, sonar, and radio signals. The use of these technologies in identifying mobile targets in a field, emitting acoustic signals, using a network of low-cost narrow band acoustic micro-sensing devices randomly dispersed over the region of interest, presents unique challenges. The effects of wind, turbulence, and temperature gradients and other environmental effects can decrease the signal to noise ratio by introducing random errors that cannot be removed through calibration. This paper presents methods for dynamic distributed signal processing to detect, identify, and track targets in noisy environments with limited resources. Specifically, it evaluates the noise tolerance of adaptive beamforming and compares it to other distributed sensing approaches. Many source localization and direction-of-arrival (DOA) estimation methods based on beamforming using acoustic sensor array have been proposed. We use the approximate maximum likelihood parameter estimation method to perform DOA estimation of the source in the frequency domain. Generally, sensing radii are large and data from the nodes are transmitted over the network to a centralized location where beamforming is done. These methods therefore depict low tolerance to environmental noise. Knowledge based localized distributed processing methods have also been developed for distributed in-situ localization and target tracking in these environments. These methods, due to their reliance only on local sensing, are not significantly affected by spatial perturbations and are robust in tracking targets in low SNR environments. Specifically, Dynamic Space-time Clustering (DSTC)-based localization and tracking algorithm has demonstrated orders of magnitude improvement in noise tolerance with nominal impact on performance. We also propose hybrid algorithms for energy efficient robust performance in very noisy environments. This paper compares the performance of hybrid algorithms with sparse beamforming nodes supported by randomly dispersed DSTC nodes to that of beamforming and DSTC algorithms. Hybrid algorithms achieve relative high accuracy in noisy environments with low energy consumption. Sensor data from a field test in the Marine base at 29 Palms, CA, were analyzed for validating the results in this paper. The results were compared to “ground truth†data obtained from GPS receivers on the vehicles.