Yoon Hak Kim

Quantizer Design for Energy-Based Source Localization in Sensor Networks

We consider energy-based source localization applications, where distributed sensors quantize acoustic signal energy readings and transmit quantized data to a fusion node, which then produces an estimate of the source location. We propose an iterative quantizer design algorithm that allows us to take into account localization accuracy for quantizer design in the framework of the generalized Lloyd algorithm. Since source coding methodologies based on the Lloyd algorithm suffer from the presence of numerous poor local optima depending on initialization of quantizers, we introduce an efficient initialization, the equally distance-divided quantizer (EDQ), designed so that quantizer partitions correspond to a uniform partitioning in terms of distance. Our simulations demonstrate that improved performance over traditional quantizer designs can be achieved by using our proposed application specific strategy.

Maximum a Posteriori (MAP)-Based Algorithm For Distributed Source Localization Using Quantized Acoustic Sensor Readings

In this paper, we propose a distributed source localization algorithm based on the maximum a posteriori (MAP) criterion, where the observations generated by each of the distributed sensors are quantized before being transmitted to a fusion node for localization. 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. In our previous work we developed quantizer design techniques aimed at optimizing localization accuracy for a given aggregate rate. In this paper we develop localization algorithms based on estimating 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. We also propose a technique, based on a weighted average of estimators, to address the case when the signal energy is unknown. Our simulation results show that our localization algorithm achieves good performance with reasonable complexity as compared with minimum mean square error (MMSE) estimation

Distributed encoding algorithm for source localization in sensor networks

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 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 the algorithm to a system where an acoustic amplitude sensor model is employed at each node for source localization. Our experiments demonstrate significant rate savings (e.g., over 30%, 5 nodes, and 4 bits per node) when our novel bin-merging algorithms are used.

Distributed estimation based on quantized data

Since standard statistical estimation methods are built on the models that treat numerical data as continuous variables, they can be inappropriate and misleading when quantization process is involved in estimation. In this paper, we propose novel distributed estimation algorithms based on the Maximum Likelihood (ML) method. Motivated by the observation that each quantized measurement corresponds to a region with which the parameter to be estimated is associated, we develop algorithms that estimates the likelihood of each of the regions rather than that of the parameter itself. Our simulation results show that the proposed algorithms achieve good performance as compared with traditional ML estimators.