Expectation maximization algorithm for calibration of ground sensor networks using a road constrained particle filter

Target tracking in ground sensor networks requires an accurate calibration of sensor positions and orientations, as well as sensor offsets and scale errors. We present a calibration algorithm based on the EM (expectation maximization) algorithm, where the particle filter is used for target tracking and a non-linear least squares estimator is used for estimation of the calibration parameters. The proposed algorithm is very simple to use in practice, since no ground truth of the target position and time synchronization are needed. In that way, opportunistic targets can also be used for calibration. For road-bound targets, a road-constrained particle filter is used to increase the performance. Tests on real data shows that a sensor position accuracy of a couple of meters is obtained from only one passing target.