Multiple Model Adaptive Nonlinear Filter for Range Estimation Using Bearing Only Tracking

Abstract

This paper discusses about the range estimation using multiple model method for single sensor bearing only tracking (BOT) in 3D. For BOT, the ownship is assumed to take a manoeuvre to gain observability in range and target state. The unknown target range was divided into uniform sub-intervals and nonlinear filters like Extended and unscented Kalman filter (EKF and UKF) with Cartesian and modified spherical coordinates (MSC) were implemented. Comparative results indicate that, UKF in MSC performs better with high computational time. This paper introduces Adaptive nonlinear filter (ANF) to get better range estimate with reduced computational time without affecting the filter performance. It is accomplished by conjoining Cartesian EKF with UKF in MSC, adaptively during the stationary and manoeuvring ownship conditions. The performance comparison was analysed using root mean square error, bias error and computational time. Simulation results reveal that ANF shows better results with reduced computational time.