Pre-processing based enhancement of DOA estimation for radar signals
Date
2020
Authors
Mulinde, R.
Kaushik, M.
Attygalle, M.
Aziz, S.M.
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This report is in fulfilment of the requirements (Deliverable D4) of phase 3 of the collaborative research project conducted by the University of South Australia (UniSA) and the RF Systems Group of the Cyber and Electronic Warfare Division of the Defence Science and Technology (DST) group. It builds on and extends the results presented in previous reports (Deliverables D1 and D2 of Phase 3). Previous reports have comprehensively investigated the estimation performance of channelized MUltiple SIgnal Classification (MUSIC) using both synthetic and experimental data. This report focuses on pre-processing techniques to enhance the direction of arrival (DOA) estimation performance of channelized MUSIC for signals with both low and high chirp rates. DOA estimation performance enhancement is achieved when incoherent aggregation of the spatial pseudospectra computed from selected bins is applied prior to DOA estimation. We have applied the pre-processing techniques to linear frequency modulated (LFM) signals. The proposed approaches show improved the DOA estimation performance most prominently for low chirp-rate LFM signals, in the low-signal-to-noise ratio (SNR) regime. These techniques exploit various signal characteristics such as signal energy and eigenvalues of the spatial covariance matrices (SCMs) extracted from the signal. In particular, in this report we • Propose different pre-processing techniques to enhance the DOA estimation performance of channelized MUSIC on narrowband and wideband LFM signals. • Compare the DOA estimation performance when pre-processing techniques are applied to channelized MUSIC with that of channelized MUSIC without pre-processing, for LFM signals with low and high chirp rates using simulated data. • Select some of the pre-processing techniques for further consideration and identify the circumstances under which these techniques are better suited. The results obtained reveal that when the proposed pre-processing-based enhancements are applied to channelized MUSIC, significant DOA estimation performance gains can be achieved especially with eigenvalue-based pre-processing. The performance gains achieved for LFM signals with low chirp rates are significantly high compared to the gains achieved for LFM signals with high chirp rates especially at low SNR values.
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Copyright 2020 The Authors