Gridless SPICE applied to parameter estimation of underwater acoustic Frequency Hopping signals

The Frequency Hopping (FH) communication technique is one of classical methods of spread spectrum communication techniques, and it is widely used in terrestrial and underwater communications due to its robustness to jamming, low probability of interception and facility in communication networking. In particular, estimating and tracking the parameters of FH signals are important tasks in underwater acoustic warfare. This paper applies gridless SPICE (GLS) to the parameter estimation of underwater acoustic FH signals, mainly focused on time-frequency pattern and hop timing. As compared to the conventional spectrogram method, the time-frequency analysis method based on gridless SPICE has better frequency acquisition for the same samples. As compared to the SLR method proposed by Daniele Angelosante, this method has better performance for estimating parameters when we do not know the hopping frequency set.

[1]  Benjamin Recht,et al.  Atomic norm denoising with applications to line spectral estimation , 2011, Allerton.

[2]  Dmitry M. Malioutov,et al.  Homotopy continuation for sparse signal representation , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Petre Stoica,et al.  Spectral Analysis of Signals , 2009 .

[4]  Nikos D. Sidiropoulos,et al.  Multiple frequency-hopping signal estimation via sparse regression , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[5]  Xian-Da Zhang,et al.  Matrix Analysis and Applications , 2017 .

[6]  Cishen Zhang,et al.  A Discretization-Free Sparse and Parametric Approach for Linear Array Signal Processing , 2013, IEEE Transactions on Signal Processing.

[7]  Lihua Xie,et al.  On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data , 2014, IEEE Transactions on Signal Processing.

[8]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[9]  W S Hodgkiss,et al.  Matched field processing with data-derived modes. , 2001, The Journal of the Acoustical Society of America.

[10]  Jian Li,et al.  Weighted SPICE: A unifying approach for hyperparameter-free sparse estimation , 2014, Digit. Signal Process..

[11]  L. Sha,et al.  Bayesian Sonar Detection Performance Prediction With Source Position Uncertainty Using SWellEx-96 Vertical Array Data , 2006, IEEE Journal of Oceanic Engineering.

[12]  Lihua Xie,et al.  Generalized Vandermonde decomposition and its use for multi-dimensional super-resolution , 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

[13]  A. Scaglione,et al.  Parameter estimation of spread spectrum frequency-hopping signals using time-frequency distributions , 1997, First IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications.

[14]  Petre Stoica,et al.  SPICE and LIKES: Two hyperparameter-free methods for sparse-parameter estimation , 2012, Signal Process..