Sensor deployment for motion trajectory tracking with a genetic algorithm

This paper focuses on determining the optimum placement of a given number of sensors for estimating the position of a moving target using range-difference measurements. We define a region of interest and generate several random trajectories with the dynamic white noise acceleration model. After obtaining those trajectories that populate the area we compute the posterior Cramér-Rao lower bound iteratively for each instant of each trajectory. Using that bound we can obtain a global measure of the mean squared error of the estimate of the position and use it as an objective function to be minimized to determine the optimum sensor placement. Finally, we determine that optimum deployment pattern using a genetic algorithm and we include an example of sensor placement using an infrared indoor positioning system.