Improved target tracking using feedback in Kalman smoother for targets moving in co-ordinated turn with unknown turn rate

Target tracking is a very important field of research as it has wide applications in defence, civilian, industrial and medical applications. Improved target state estimate without lag by feedback in Kaiman smoother is an approach that uses information from a batch of measurements in recursive estimation by providing a feedback in the Kaiman smoother. This approach provides an estimate without delay while using measurements from multiple time frames and improves the target state estimation accuracy. In this paper this method of batch processing and providing estimates without delay, has been used to track a target that moves in coordinated turns with an unknown turn rate. The results are compared with conventional Kaiman filter. The Monte Carlo simulation results with the Improved target state estimate without lag by feedback in Kaiman smoother approach shows that the estimation accuracy can be significantly improved compared to ordinary Kaiman filter based estimates.