Frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems due to its capability of low probability of intercept, reduced interference, and desirable ambiguity property. In this paper, we consider the blind estimation of the instantaneous FH spectrum without the knowledge of hopping patterns. The FH signals are analyzed in the joint time-frequency domain, where FH signals manifest themselves as sparse entries, thus inviting compressive sensing and sparse reconstruction techniques for FH spectrum estimation. In particular, the signals' piecewise-constant frequency characteristics are exploited in the reconstruction of sparse quadratic time-frequency representations. The Bayesian compressive sensing methods are applied to provide high-resolution frequency estimation. The FH spectrum characteristics are used in the design of signal-dependent kernel within the framework of structure-aware sparse reconstruction.
In this paper, we propose a nonstationary jammer suppression method for GPS receivers when the signals are sparsely sampled. Missing data samples induce noise-like artifacts in the time-frequency (TF) distribution and ambiguity function of the received signals, which lead to reduced capability and degraded performance in jammer signature estimation and excision. In the proposed method, a data-dependent TF kernel is utilized to mitigate the artifacts and sparse reconstruction methods are then applied to obtain instantaneous frequency (IF) estimation of the jammers. In addition, an error tolerance of the IF estimate is applied is applied to achieve robust jammer suppression performance in the presence of IF estimation inaccuracy.
KEYWORDS: Statistical analysis, Thallium, Monte Carlo methods, Signal to noise ratio, Neodymium, Silicon, Super resolution, Surveillance, Sensors, Measurement devices
The coprime sampling scheme allows signal frequency estimation through two sub-Nyquist samplers where the down-sampling rates M and N are coprime integers. By considering the difference set of this pair of O(M + N ) physical samples, O(MN ) consecutive virtual samples can be generated. In this paper, a generalized coprime sampling technique is proposed by using O(M + pN ) samples to generate O(pMN ) virtual samples, where p is an integer argument. As such, the existing coprime sampling techniques are represented as a special case of a much broader and generalized scheme. The analytical expressions of the number of virtual samples, frequency resolution and the corresponding latency time are derived. The effectiveness of the proposed technique is verified using simulation results.
KEYWORDS: Sensors, Signal detection, Antennas, Wave propagation, Point spread functions, Signal to noise ratio, Silicon, Array processing, Electromagnetic radiation
Coprime array, which utilizes a coprime pair of uniform linear subarrays, is an attractive structure to achieve sparse array configurations. Alternatively, effective coprime array configurations can be implemented using a uniform linear array with two coprime sensing frequencies. This enables the integration of the coprime array and filter concepts to achieve high capabilities in meeting system performance and complexity constraints. This paper examines its performance for direction-of-arrival estimations. In particular, we analyze the number of detectable signals and the estimation accuracy as related to the array configurations and sensing frequencies.
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