Conditioned measurement fused estimate Unscented Kalman filter for underwater target tracking using acoustic signals captured by Towed array

In the paper, an algorithm named Robust Unscented Kalman filter (R-UKF) is proposed to handle the popular ocean issue called underwater passive object/target tracking in a more efficient manner than the conventional algorithms. This R-UKF algorithm using acoustic signals supplied by a Towed array is developed when two novel Techniques named as Estimate Fusion (EF) Method and Measurement Conditioning (MC) Method are applied simultaneously to the existing Unscented kalman filter (UKF). Estimate Fusion method and Measurement Conditioning methods operate on the principles of weighted averaging of measurements in space and time respectively. EF Method contributed to the improvement by believing that the collective opinion about state estimation is much better than the individual opinions. This is accomplished by relying on Multiple sensors of TA and Multiple Intermediate estimators instead of single one. On the other hand MC method contributed to the improvements by application of the soothed measurements instead of traditional ones. The soothing is possible by weighted averaging of the current and track of past sensor data. Montecarlo simulations in Matlab(R2009a) shows that, R-UKF display better performance that its base algorithm UKF. Moreover, Optimal Performance (produce low estimation errors without demanding complex processors) of R-UKF makes it even more attractive. (C) 2020 Elsevier Ltd. All rights reserved.