Optimal Sensor Placement for RSS-Based Localization with Biased Measurement Model

Wireless localization using received signal strength (RSS) has successfully applied in a variety of areas due to its merits, e.g., easy implementation. For RSS-based localization, sensor placement is as important as localization algorithm, and the optimal placement has been investigated by using certain criterions. However, the existing work mainly focus on the bias-free measurement model, i.e., the measurement errors comprise random noises only and sensor biases are not considered. In this paper, we investigate the optimal sensor placement using biased RSS measurements. A-optimality criterion is utilized to formulate the optimization problem with respect to the optimal sensor placement. By minimizing the trace of the Cramer-Rao lower bound (CRLB) for the biased model, the optimal strategies for placing sensors are derived. Illustrative examples validate the proposed method of sensor placement, in which the localization accuracies are significantly improved for the same localization algorithms, compared to other scenarios.