Detecting flying objects in synthetic aperture radar images using Moving Target Indicator methods

Abstract

The growing proliferation of synthetic aperture radar (SAR) sensors brings the tantalising prospect of extending their utility into ‘novel’ applications. One potential extension is the detection of fast moving and accelerating flying objects in SAR imagery. However, since SAR image formation typically assumes the scene to be static over the coherent processing interval, moving objects give rise to blurred point spread functions, significant range migration and even potential aliasing of target signatures. The result is reduced target to clutter ratio (TCR) and poor detection performance. Successful detection of airborne targets thus requires compensation for potentially large target acceleration and velocity values observed over the comparatively long dwell times typical of practical SAR collection paradigms. This paper considers this problem and presents two main ideas to achieve this goal: a carefully constructed Moving Target Indicator (MTI) detection method implemented using real-world Ingara SAR data, and a theoretical ground clutter suppression method. The MTI detection method combines several well-known techniques for the flying target detection problem: interferometric processing, clutter suppression, and autofocus, and provides an extended acceleration phase compensation technique for highly accelerating targets such as planes. This proposed processing pipeline has been applied to experimental data of a plane during take off (a challenging Doppler unambiguous moving target), with the goal of continued detecting and tracking of this target. A generalised SAR signal model is presented that parameterises a flying moving target signature in terms of range and azimuthal target velocities and accelerations. Data driven approaches for estimating these motion parameters are examined and applied to experimental data acquired with the Ingara SAR sensor. The detection method was found to improve TCR by around 6 dB, along with superior detection and tracking performance. Following this, a theoretical study into suppressing ground clutter via multi-channel cross-track interferometry is investigated. Three separate ground clutter suppression methods, coherent subtraction, conventional beamforming, and minimum variance distortionless response (MVDR) beamformer, are presented then analysed using stochastic simulations. The MVDR adaptive beamformer method was found to provide the best performance for the scenario simulated.