In this lecture, we present the principles of space-time adaptive processing (STAP) for radar, applied to moving target indication. We discuss the properties of optimum STAP, as well as problems associated with estimating the adaptive weights not encountered with spatial-only processing (i.e. beamforming).

In this lecture, we present some suboptimum STAP algorithms for radar, applied to moving target indication. In addition, we show some MTI results obtained with the multi-channel airborne experimental radar AER-II of FGAN-FHR.

Space-time adaptive processing (STAP) is a signal processing technique that was originally developed for detecting slow-moving targets using airborne radars. The general principle of STAP is as follows. The radar transmits a train of M coherent pulses. The echoes from potential targets (and clutter) are collected at each of the N elements of an antenna array. Separate receiver chains are attach...

In this research, an efficient Galerkin Finite Volume Method (GFVM) along with the h–refinement adaptive process and post–processing error estimation analysis is presented for fracture analysis. The adaptive strategy is used to produce more accurate solution with the least computational cost. To investigate the accuracy and efficiency of the developed model, the GFVM is compared with two versio...

Although traditional signal-based structural health monitoring algorithms have been successfully employed for small structures, their application for large and complex bridges has been challenging due to non-stationary signal characteristics with a high level of noise. In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial...