Signal of opportunity (SOP) has become an attractive source of navigation in the absence of global navigation satellite system (GNSS). However, in some typical GNSS-limited environments, such as deep urban canyons, the SOP positioning is challenged by low signal-to-noise ratio (SNR), rapidly time-varying channels, and gain/phase uncertainties. To overcome these challenges, we propose a sparse direction of arrival (DOA) estimation method specifically designed for SOP positioning. Under the conditions of low SNR and limited number of snapshots, we conduct detailed theoretical derivations and simulation experiments to analyze the negative impact of gain and phase uncertainties on sparse DOA estimation. The analysis indicates that these uncertainties can lead to an increase in the number or height of spurious peaks in the DOA spatial spectrum, thereby significantly reducing the accuracy of DOA estimation. To address this issue, we propose a non-iterative sparse DOA estimation method that combines blind source separation (BSS) and singular value decomposition (SVD) techniques. The BSS algorithm accurately determines the number of SOPs using a single sensor, effectively eliminating the impact of gain and phase uncertainties between sensors. Once the number of SOPs is obtained, we can introduce the SVD algorithm to further enhance the DOA estimation performance under low SNR conditions. Simulation results validate the effectiveness of the proposed method in DOA estimation, showcasing its excellent robustness and self-calibration characteristics while maintaining reasonable computational costs. The introduction of this method provides a new solution to the navigation and positioning problem in GNSS-denied environments.
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