Temporal fusion in multi-sensor target tracking systems

Abstract

For a multi-sensor tracking system, the effects of temporally staggered sensors are investigated and compared with synchronous sensors. To make fair comparisons, a new metric, the average estimation error variance, is defined. Many analytical results are derived for sensors with equal measurement noise variance. Temporally staggered sensors always result in a smaller average error variance than synchronous sensors. The corresponding optimal staggering pattern is such that the sensors are uniformly distributed over time. For sensors with different measurement noise variances, the optimal staggering pattern can be found numerically. Intuitive guidelines on selecting optimal staggering pattern have been presented for different target tracking scenarios.