Optimal Threshold for Locating Targets Within a Surveillance Region Using a Binary Sensor Network

This paper considers the design of the optimal threshold for locating targets within a specified surveillance range using a network of binary sensors. The threshold was determined by minimizing a cost function representing the summation of the variances of estimation errors for the x and y coordinates of the target. The cost function was evaluated using multiple integration over the specified ranges for target power and target location. A geometrical interpretation of the optimal thresholds for targets with known power and location is presented to offer insight into the problem. The optimal threshold was validated in Monte Carlo simulations using a field of sensors having a uniform grid layout. In simulations, the summation of the variances of the estimation errors achieved using the optimal threshold approached the minimum of the cost function.