Traditional space time adaptive processors for radar target detection require a training data set which is usually drawn from adjacent range gates. Clutter heterogeneity, however, can severely limit the available training sample support and consequently degrade the detection performance. The SDS algorithms, on the other hand, overcome this problem by operating solely on the test data without recourse to training data. In this paper we evaluate both of these approaches, in particular the AMF and MLED, using the MCARM data set. We illustrate the performance degradation of the AMF that results from the clutter heterogeneity and the corresponding advantage of the MLED. We also show that a calibration step of the spatial steering vectors results in significant performance improvement of all of the algorithms considered here.
['Elias Aboutanios', 'Bernie Mulgrew']
Evaluation of the single and two data set STAP detection algorithms using measured data