One-step fixed-lag interacting multiple model (IMM) smoothing for alignment of asynchronous sensors

An algorithm is presented for the on-line relative alignment of two 3D sensors (where a 3D sensor is one that measures range, bearing, and elevation) using common targets that are tracked by both sensors. The target data reported by the sensors are usually not time coincident and, consequently, the estimates from the tracking filters for the sensors will be at different times. Since the alignment algorithm requires time-coincident target data from the sensors, a one-step predictor is used to time translate the track estimates from one of the sensors to the times of the track estimates from the other sensor. A one-step fixed-lag smoothing algorithm is then used to improve the accuracy of the predicted estimate by processing the measurement from the stage one-step ahead. The time-coincident track estimates are passed to an alignment algorithm that estimates the alignment errors and then uses these estimates to compensate for the effects of the alignment errors in the multi-sensor data. For illustrative purposes, simulations will be used to compare the performances of an alignment algorithm based on the one-step predictor and one-step fixed lag smoother to one based solely on the one-step predictor.