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This paper introduces an adaptive radar waveform technique where a standard LFM waveform is used as an initial seed. The seed waveform is changed using a phase-only adaptive technique to minimize the resulting waveform's power in specific areas associated with in-band and adjacent-band spectral regions. Adjacent-band power minimization reduces interference to other spectral users, while in-band power...
We introduce a methodology that creates a new class of reduced-rank adaptive cascaded canceller algorithms. Two example algorithms illustrate the benefits of the proposed technique. It consists of combining a novel soft-weighting technique with an existing reiteration technique. Denoted as soft-weighting and reiteration (SWR), its use significantly improves the convergence performance of cascaded...
Auto-regressive (AR) models are used to form temporal and/or spatial super-resolution spectra for source signal detection and estimation. An AR spectrum is considered a super-resolution technique that can distinguish signal frequencies or angular locations with higher resolution, and often using many fewer data samples, as compared to Fourier spectral techniques. This paper presents a novel method...
Adaptive radar requires independent and identically distributed (i.i.d.) training data, or snapshots, in order to obtain fast SINR convergence performance in the presence of correlated interference such as jamming and/or clutter returns. Targets, clutter discretes, and impulsive jamming are examples of non i.i.d., real-world data components that corrupt interference training data. Such data are considered...
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