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access icon free Aliasing-free micro-Doppler analysis based on short-time compressed sensing

© The Institution of Engineering and Technology
Received 29/12/2012, Accepted 22/08/2013, Revised 21/08/2013, Published 03/10/2013

Time–frequency distribution (TFD) has been widely used for micro-Doppler analysis in radar signal processing. However, the spectrogram will suffer from aliasing if the maximum Doppler frequency exceeds half of the pulse repetition frequency, which may lead to false estimation of the targets' kinematic properties. In this study, by transmitting a series of random pulse repetition interval (RPRI) pulses, a concise TFD approach named short-time compressed sensing (STCS) is proposed for aliasing-free micro-Doppler analysis. In STCS, precise analysis and synthesis of the random sampling time series can be achieved by exploiting the signal's sparsity in the frequency domain. Furthermore, adaptive to the data, the widths of the particular rectangle windows are determined by sequential processing with a proper optimisation rule. To speed up the STCS procedure, the smoothed L0 algorithm is chosen for sparse recovery, where the pseudoinverse of the dictionaries can be calculated iteratively. The simulation results indicate that the proposed STCS approach can achieve both preferable TFD and acceptable computational cost. The effectiveness of the STCS is finally verified by the application for micro-Doppler estimating in RPRI radar.

Inspec keywords: time series; optimisation; time-frequency analysis; compressed sensing; sampling methods; iterative methods; radar signal processing; Doppler radar

Other keywords: random pulse repetition interval pulses; random sampling time series; maximum Doppler frequency; rectangle windows; time-frequency distribution; frequency domain; pulse repetition frequency; aliasing-free micro-Doppler analysis; radar signal processing; smoothed L0 algorithm; short-time compressed sensing; proper optimisation rule; STCS; concise TFD approach; RPRI radar

Subjects: Optimisation techniques; Signal processing and detection; Radar equipment, systems and applications; Interpolation and function approximation (numerical analysis); Other topics in statistics; Mathematical analysis

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