Abstract
The problem of obtaining high range resolution (HRR) profiles for non-cooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radars’ range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined -norm regularisation solvers was explored to estimate the offsets between the radars’ channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.
Highlights
The construction of high range resolution profiles (HRRP) of targets is a precursor to feature extraction for automatic target recognition (ATR), and normally requires the employment of a high-bandwidth waveform following detection by a lower resolution radar mode
We focus on the problem of bandwidth stitching for radar high-resolution range profiling and explore the use of sparse reconstruction to deal with the phase error problem
Since a smaller value of earth mover’s distance (EMD) corresponds to a higher level of similarity between the true and reconstructed range profiles, this observation indicates that the reconstructed range profile obtained by pruned orthogonal matching pursuit (POMP) is closer to the ground-truth range profiles than those obtained from the OMP and l1 -norm regularised optimisation methods
Summary
The construction of high range resolution profiles (HRRP) of targets is a precursor to feature extraction for automatic target recognition (ATR), and normally requires the employment of a high-bandwidth waveform following detection by a lower resolution radar mode. Compressive sensing and sparse reconstruction were exploited in [17,19,21,22] to address the problem of gaps in the data both in slow-time and in frequency for inverse synthetic aperture radar (ISAR) imaging These works assumed that the data were coherent across different sub-bands and that there were no model uncertainties. The work [23] took account of the possible lack of mutual coherence between the radars operating on the different sub-bands arising from incorrect timing synchronisation, or, equivalently, errors in antenna phase’s centre-relative locations This is achieved by fitting an ultra-wideband all-pole signal model to the mutually-coherent sub-bands, which is used for bandwidth interpolation and extrapolation prior to recovering the range profile by means of an inverse Fourier transform.
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