Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency Hopping Signals Based on Single Source Points Detection

Author:

Zhang Chaozhu,Wang Yu,Jing Fulong

Abstract

This paper considers the complex-valued mixing matrix estimation and direction-of-arrival (DOA) estimation of synchronous orthogonal frequency hopping (FH) signals in the underdetermined blind source separation (UBSS). A novel mixing matrix estimation algorithm is proposed by detecting single source points (SSPs) where only one source contributes its power. Firstly, the proposed algorithm distinguishes the SSPs by the comparison of the normalized coefficients of time frequency (TF) points, which is more effective than existing detection algorithms. Then, mixing matrix of FH signals can be estimated by the hierarchical clustering method. To sort synchronous orthogonal FH signals, a modified subspace projection method is presented to obtain the DOAs of FH. One superiority of this paper is that the estimation accuracy of the mixing matrix can be significantly improved by the proposed SSPs detection criteria. Another superiority of this paper is that synchronous orthogonal FH signals can be sorted in underdetermined condition. The experimental results demonstrate the efficiency of the two proposed algorithms.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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1. A Novel Underdetermined Blind Source Separation Algorithm of Frequency-Hopping Signals via Time-Frequency Analysis;IEEE Transactions on Circuits and Systems II: Express Briefs;2023-11

2. Frequency hopping signals tracking and sorting algorithm for military radio networks;2023 International Symposium on Electromagnetic Compatibility – EMC Europe;2023-09-04

3. Diffuseness Estimation-Based SSTP Detection for Multiple Sound Source Localization in Reverberant Environments;Circuits, Systems, and Signal Processing;2023-03-10

4. Multi-Source Localization Using Optimized Time-Frequency Representation and Sparsity Component Analysis;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023

5. Hybrid clustering algorithm based on ISFLA and PFCM with application to UBSS;2022 34th Chinese Control and Decision Conference (CCDC);2022-08-15

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