Airborne Radar Ground Clutter Suppression Using Multitaper Spectrum Estimation : Comparison with Traditional Method

Abstract: During processing of data received by an airborne radar one of the issues is that the typical signal echo from the ground produces a large perturbation. Due to this perturbation it can be difficult to detect targets with low velocity or a low signal-to-noise ratio. Therefore, a filtering process is needed to separate the large perturbation from the target signal. The traditional method include a tapered Fourier transform that operates in parallel with a MTI filter to suppress the main spectral peak in order to produce a smoother spectral output. The difference between a typical signal echo produced from an object in the environment and the signal echo from the ground can be of a magnitude corresponding to more than a 60 dB difference. This thesis presents research of how the multitaper approach can be utilized in concurrence with the minimum variance estimation technique, to produce a spectral estimation that strives for a more effective clutter suppression. A simulation model of the ground clutter was constructed and also a number of simulations for the multitaper, minimum variance estimation technique was made. Compared to the traditional method defined in this thesis, there was a slight improvement of the improvement factor when using the multitaper approach. An analysis of how variations of the multitaper parameters influence the results with respect to minimum detectable velocity and improvement factor have been carried out. The analysis showed that a large number of time samples, a large number of tapers and a narrow bandwidth provided the best result. The analysis is based on a full factorial simulation that provides insight of how to choose the DPSS parameters if the method is to be implemented in a real radar system.