Time delay estimation (TDE) is an active research area due to its importance in a wide range of applications, such as target detection and transmitter identification. At present, the majority of the radar systems are using multistatic architecture because of its improved detection and tracking performance. In multistatic radar systems, where more than one transmitter and receivers are deployed, it is a challenging task to separate multiple echoes at the receiver to estimate the required parameters and identify the number of transmitters. The delay estimation process becomes more complicated when closely spaced multiple targets are involved in the detection process. In this study, various issues associated with conventional matched filtering-based target detection techniques are investigated in terms of nonlinear time-frequency representation with ambiguity function and cross-ambiguity functions. The problems of delay estimation and cross-range resolution are analyzed through simulations in a multistatic radar system.
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.
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