Sparse Semi-Parametric Estimation of Harmonic Chirp Signals

Summary, in English

In this work, we present a method for estimating the parameters detailing an unknown number of linear, possibly harmonically related, chirp signals, using an iterative sparse reconstruction framework. The proposed method is initiated by a re-weighted group-sparsity approach, followed by an iterative relaxation-based refining step, to allow for high resolution estimates. Numerical simulations illustrate the achievable performance, offering a notable improvement as compared to other recent approaches. The resulting estimates are found to be statistically efficient, achieving the corresponding Cram´er-Rao lower bound.

• Author: Johan Swärd; Johan Brynolfsson; Andreas Jakobsson; Maria Sandsten
• Department/s: Mathematical Statistics; Statistical Signal Processing Group; Biomedical Modelling and Computation; eSSENCE: The e-Science Collaboration
• Publishing year: 2016
• Language: English
• Pages: 1798-1807
• Publication/Series: IEEE Transactions on Signal Processing
• Volume: 64
• Issue: 7
• Full text: - 1 MB; Download statistics
• Links: http://dx.doi.org/10.1109/TSP.2015.2507538
• Document type: Journal article
• Publisher: IEEE - Institute of Electrical and Electronics Engineers Inc.
• Topic: Signal Processing
• Status: Published
• Research group: Statistical Signal Processing; Statistical Signal Processing Group; Biomedical Modelling and Computation
• ISBN/ISSN/Other: ISSN: 1053-587X