Quasi maximum likelihood estimator of polynomial phase signals for compressed sensed data |
| |
| Affiliation: | 1. University of Montenegro, Electrical Engineering Department, Cetinjski put bb, 81000 Podgorica, Montenegro;2. National Aerospace University, Department of Transmitters, Receivers and Signal Processing, Kharkov, Ukraine;3. Petroleum Institute, Ruwais Building, Abu Dhabi, United Arab Emirates;1. Electronics Department, Faculty of Engineering, University of Djillali Liabes, BP 89 Sidi bel Abbes, Algeria;2. Department of Measurement Control & Information Technology, School of Instrumentation Science & Optoelectronics Engineering, BeiHang University, 100083 Beijing, PR China;1. School of Logistics Engineering, Wuhan University of Technology, China;2. School of Mechanical and Electrical Engineering, Wuhan University of Technology, China;3. School of Electronic and Information Engineering, Southwest University, China |
| |
| Abstract: | Several papers in the literature cover parameter estimation of frequency modulated (FM) signals under reduced number of signal samples with respect to the Nyquist/Shannon criterion, i.e., within the compressive sensing (CS) framework. However, scope of these papers is mainly limited to sinusoids or sum of sinusoids. In this paper, the CS framework is extended to parameter estimation of higher order polynomial phase signals (PPSs) using the quasi-maximum likelihood (QML) estimator and robust short-time Fourier transform (STFT). The considered signal is assumed to be non-uniformly sampled PPS with smaller number of samples with respect to the Nyquist/Shannon criterion. However, the proposed technique can also be generalized to uniformly sampled signals with missing or unreliable samples. |
| |
| Keywords: | Polynomial phase signals Parameter estimation Compressive sensing |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
