Mixing matrix estimation method for dual‐channel time‐frequency overlapped signals based on interval probability

Abstract

For dual‐channel time‐frequency (TF) overlapped signals with low sparsity in underdetermined blind source separation (UBSS), this paper proposes an effective method based on interval probability to estimate and expand the types of mixing matrices. First, the detection of TF single‐source points (TF‐SSP) is used to improve the TF sparsity of each source. For more distinguishability, as the ratios of the coefficients from different columns of the mixing matrix are close, a local peak‐detection mechanism based on interval probability (LPIP) is proposed. LPIP utilizes uniform subintervals to optimize and classify the TF coefficient ratios of the detected TF‐SSP effectively in the case of a high level of TF overlap among sources and reduces the TF interference points and redundant signal features greatly to enhance the estimation accuracy. The simulation results show that under both noiseless and noisy cases, the proposed method performs better than the selected mainstream traditional methods, has good robustness, and has low algorithm complexity.

A Sorting Method for Radar Echo Extraction Based on Observation Submatrix

“…In this paper, we employ the estimate method given by Liu et al. (2019), which enables estimation without previous knowledge of the number of sources, while maintaining a stable accuracy by adjusting the size of probability interval. Notably, the size of the estimated matrix $$\hat{\mathbf{A}}$$ is typically larger than the true mixing matrix A , and that adds some computational overhead but has no effect the separation performance.…”

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Recently, the methods based on sparse component analysis (SCA) have been proven to be an excellent solution for radar anti-jamming, which approximately consist of two steps: (a) the estimation of the observed mixing matrix; (b) the separation and recovery of the radar echo using the estimated mixing matrix. Since the matrix estimation technique based on observed signals is rather established (Liu et al, 2019;Sun et al, 2016), the separation and recovery method of echo remains a research hotspot. The orthogonal matching pursuit based on greedy or distinct sampling strategy in time-frequency domain has been advocated in earlier studies (Mohimani et al, 2009;Stanković et al, 2013), in these papers, the primary components of the sources are extracted successively to recover the main signal features of the target echo.

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A Novel Countermeasure Method for Radar Coherent Jamming Based on Signal Feature Transformation

“…Prior to source separation, the observed mixing matrix estimation is considered. In this paper, we employ the estimate method described in [10], which enables estimation to be completed without previous knowledge of the number of sources, while maintaining a stable accuracy that can be regulated by the size of the probability interval. Notably, the size of the estimated matrix 􏽢 A is typically larger than the true mixing matrix A, and the column vectors are enlarged to provide some virtual sources, which adds some computational overhead but has no efect on the separation performance.…”

“…Recently, the methods based on sparse component analysis (SCA) have been proven to be an excellent solution for radar antijamming, which approximately consist of two steps: (1) the estimation of the observed mixing matrix; (2) the separation and recovery of the target echo using the estimated mixing matrix. Although the matrix estimation technique based on observed signals is rather established [10,11], the separation and recovery method of target echo remains a research hotspot. Te orthogonal matching pursuit based on a greedy or distinct sampling strategy in the time-frequency domain has been advocated in [12,13], and in these papers, the primary components of the target signal are extracted successively to recover the main signal features of the target echo.…”

Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation

“…Taking into account the implementation cost and space arrangement, BSS with a limited number of sensors [25] should be more attractive and appropriate for practical scenarios, e.g., speech enhancement systems [26] and multiple-input-multiple-output radar or communication systems [27], where low-cost and small-space configuration are highlighted. Despite the extensive prior works and applications laying a foundation for BSS, dual-sensor BSS has not been effectively addressed in the literature.…”