Detection and Parameter Estimation of Air Maneuvering Targets via Reconstructing Time Samples

Space-Time Adaptive Processing (STAP) is an effective method for moving target detection in airborne radar. However, the Doppler frequency will change with time when the target has strong maneuvering, which would degrade the coherent integration performance of STAP significantly. For the detection and the parameter estimation of air maneuvering targets, a new algorithm is proposed in this paper, which combines STAP with FRactional Fourier Transform (FRFT). Space samples are used to reconstruct the time samples, which is equivalent to increasing the number of time samples within a Coherent Processing Interval (CPI). Consequently, the problem of the poor estimation performance by using the FRFT, which is caused by the limited number of time samples of airborne radar within a CPI, can be resolved. Numerical examples are provided to demonstrate the performance of the proposed algorithm.