Convergence of a block coordinate descent method for nondifferentiable minimization
Journal of Optimization Theory and Applications
Nonlinear Optimization
Stochastic modeling and particle filtering algorithms for tracking a frequency-hopped signal
IEEE Transactions on Signal Processing
On recovery of sparse signals via l1 minimization
IEEE Transactions on Information Theory
Sampling piecewise sinusoidal signals with finite rate of innovation methods
IEEE Transactions on Signal Processing
Coherence-based performance guarantees for estimating a sparse vector under random noise
IEEE Transactions on Signal Processing
Blind high-resolution localization and tracking of multiplefrequency hopped signals
IEEE Transactions on Signal Processing
Hybrid FM-polynomial phase signal modeling: parameter estimationand Cramer-Rao bounds
IEEE Transactions on Signal Processing
Product high-order ambiguity function for multicomponentpolynomial-phase signal modeling
IEEE Transactions on Signal Processing
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals
IEEE Transactions on Signal Processing
Joint hop timing and frequency estimation for collision resolution in FH networks
IEEE Transactions on Wireless Communications
Decoding by linear programming
IEEE Transactions on Information Theory
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Frequency hopping (FH) signals have well-documented merits for commercial and military applications due to their near-far resistance and robustness to jamming. Estimating FH signal parameters (e.g., hopping instants, carriers, and amplitudes) is an important and challenging task, but optimum estimation incurs an unrealistic computational burden. The spectrogram has long been the starting non-parametric estimator in this context, followed by line spectra refinements. The problem is that hop timing estimates derived from the spectrogram are coarse and unreliable, thus severely limiting performance. A novel approach is developed in this paper, based on sparse linear regression (SLR). Using a dense frequency grid, the problem is formulated as one of under-determined linear regression with a dual sparsity penalty, and its exact solution is obtained using the alternating direction method of multipliers (ADMoM). The SLR-based approach is further broadened to encompass polynomial-phase hopping (PPH) signals, encountered in chirp spread spectrum modulation. Simulations demonstrate that the developed estimator outperforms spectrogram-based alternatives, especially with regard to hop timing estimation, which is the crux of the problem.