Publications

We consider the intersection between nonrepeating random FM (RFM) waveforms and practical forms of optimal mismatched filtering (MMF). Specifically, the spectrally-shaped inverse filter (SIF) is a well-known approximation to the least-squares (LS-MMF) that provides significant computational savings. Given that nonrepeating waveforms likewise require unique nonrepeating MMFs, this efficient form is an attractive option. Moreover, both RFM waveforms and the SIF rely on spectrum shaping, which establishes a relationship between the goodness of a particular waveform and the mismatch loss (MML) the corresponding filter can achieve. Both simulated and open-air experimental results are shown to demonstrate performance.

Abstract not provided.

An algorithm is developed based on Edmund K. Miller's Model-Based Parameter Estimation (MBPE) technique to mitigate the effects of missing or corrupted data in random regions of wideband linear frequency modulated (LFM) radar signals. Two methods of applying MBPE in the spectral/frequency domain are presented that operate on either the full complex data or separated magnitude/phase data, respectively. The final algorithm iteratively applies MBPE using the latter approach to re-generate results in the corrupted regions of a windowed LFM signal until the difference is minimized relative to un-corrupted data. Several sets of simulations were conducted across many randomized gap parameters where impulse response (IPR) impacts are summarized. Conditions where the algorithm successfully improved the IPR for a single target are provided. The algorithm's effectiveness on multiple targets, especially when the corrupted regions are relatively large compared to the overall bandwidth of the signal, are also explored.

Abstract not provided.