An effective method is developed for selecting sample snapshots for the training data used to compute the adaptive weights for an adaptive match filter (AMF); specifically a space/time adaptive processing (STAP) airborne radar configuration is considered. In addition, a new systematic robust adaptive algorithm is presented and evaluated against interference scenarios consisting of jamming, nonhomogeneous airborne clutter (generated by the Research Laboratory STAP (RLSTAP) or knowledge-aided sensor signal processing and expert reasoning (KASSPER) high-fidelity clutter models or using the multi-channel airborne radar measurement (MCARM) clutter data base), internal system noise, and outliers (which could take the form of targets themselves). The new algorithm arises from empirical studies of several combinations of performance metrics and processing configurations. For culling the training data, the generalized inner product (GIP) and adaptive power residue (APR) are examined. In addition two types of data processing methods are considered and evaluated: sliding window processing (SWP) and concurrent block processing (CBP). For SWP, a distinct adaptive weight is calculated for each cell-under-test (CUT) in a contiguous set of range cells. For one configuration of CBP, two distinct weights are calculated for a contiguous set of CUTs. For the CBP, the CUTs are in the initial training data and there are no guard cells associated with the CUT as there would be for SWP. Initial studies indicate that the combination of using the fast maximum likelihood (FML) algorithm, reiterative censoring, the APR metric, CBP, the two-weight method, and the adaptive coherence estimation (ACE) metric (we call this the FRACTA algorithm) provides a basis for effective detection of targets in nonhomogeneous interference. For the KASSPER data, FRACTA detects 154 out of 268 targets with one false alarm (P F ¼ 3 £ 10 ¡5) whereas the FML algorithm with SWP detects 11 with one false alarm. The clarvoyant processor (where each range cell's covariance matrix is known) detects 192 targets with one false alarm.

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A median cascaded canceller (MCC) is introduced as a robust multichannel adaptive array processor. Compared with sample matrix inversion (SMI) methods, it is shown to significantly reduce the deleterious effects of impulsive noise spikes (outliers) on convergence performance of metrics such as (normalized) output residue power and signal to interference-plus-noise ratio (SINR). For the case of no outliers, the MCC convergence performance remains commensurate with SMI methods for several practical interference scenarios. It is shown that the MCC offers natural protection against desired signal (target) cancellation when weight training data contains strong target components. In addition, results are shown for a high-fidelity, simulated, barrage jamming and nonhomogenous clutter environment. Here the MCC is used in a space-time adaptive processing (STAP) configuration for airborne radar interference mitigation. Results indicate the MCC produces a marked SINR performance improvement over SMI methods.

A novel, robust adaptive processor is introduced, based on reiterative application of the Median Cascaded Canceller (MCC). The MCC, though a highly robust adaptive processor, has a convergence rate that generally is dependent on the effective rank of the interference-plus-noise covariance matrix.The Reiterative Median Cascaded Canceller (RMCC) introduced here exhibits the highly desirable combination of 1) convergence-robustness to outlierdtargets in adaptive weight training data, like the MCC, and 2) fast convergence performance independent of the interference-plus-noise covariance matrix and at a rate commensurate with the Sample Matrix Inversion (SMI) algorithm, unlike the MCC. Both simulated data as well as measured airborne radar data from the MCARM Space-Time Adaptive Processing (STAP) database are used to show performance enhancements. It is concluded that the RMCC adaptive processor is a highly robust replacement for the SMI adaptive processor and all its equivalent implementations.

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Previous work has addressed adaptive radar waveforms that may be designed to be spectrally orthogonal to a received, in-band, time-varying, interference environment. This paper introduces a different adaptive waveform technique whereby a standard LFM waveform is modified to minimize its adjacent-band interference to other spectral users. In addition, results are shown for in-band and/or adjacent-band spectral suppression for improving radar performance and minimizing interference to other spectral users while maintaining good ambiguity function performance. Ambiguity functions are plotted for each case showing good approximation to the ideal LFM ambiguity function.

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