Distributed algorithms for source localization using quantized sensor readings
Antonio Ortega, Yoon Hak Kim · 2007
We consider sensor-based distributed source localization applications, where sensors transmit quantized data to a fusion node, which then produces an estimate of the source location. For this application, the goal is to minimize the amount of information that the sensor nodes have to exchange in order to attain a certain source localization accuracy. We propose an iterative quantizer design algorithm that allows us to take into account the localization accuracy for quantizer design. We show that the quantizer design should be “application-specific” and a new metric should be defined to design such quantizers. In addition, we address, using the generalized BFOS algorithm, the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source. We also propose a distributed encoding algorithm that is applied after quantization and achieves significant rate savings by merging quantization bins. The bin-merging technique exploits the fact that certain combinations of quantization bins at each node cannot occur because the corresponding spatial regions have an empty intersection. We apply these algorithms to a system where an acoustic amplitude sensor model is employed at each sensor for source localization. For this case, we propose a distributed source localization algorithm based on the maximum a posteriori (MAP) criterion. If the source signal energy is known, each quantized sensor reading corresponds to a region in which the source can be located. Aggregating the information obtained from multiple sensors corresponds to generating intersections between the regions. We develop algorithms that estimate the likelihood of each of the intersection regions. This likelihood can incorporate uncertainty about the source signal energy as well as measurement noise. We show that the computational complexity of the algorithm can be significantly reduced by taking into account the correlation of the received quantized data. Our simulations show the improved performance of our quantizer over traditional quantizer designs and that our localization algorithm achieves good performance with reasonable complexity as compared to minimum mean square error (MMSE) estimation. They also show that an optimized rate allocation leads to significant rate savings (e.g., over 60%) with respect to a rate allocation that uses the same rate for each sensor, with no penalty in localization efficiency. In addition, they demonstrate rate savings (e.g., over 30%, 5 nodes, 4 bits per node) when our novel bin-merging algorithms are used.