研究目的
To develop a sensor localization technique for highly-deformed partially calibrated arrays with multiple moving targets, addressing phase ambiguities and improving estimation accuracy.
研究成果
The proposed GESPRIT-based algorithm effectively estimates sensor positions in highly-deformed arrays by leveraging phase differences and resolving ambiguities through subspace orthogonality and target movement. It outperforms the CMA method and approaches the CRB, especially with exact DOA information. Future work could focus on extending this to more complex array geometries or real-world applications.
研究不足
The method assumes the first Mc sensors are well-calibrated and requires multiple moving targets to resolve ambiguities. Performance degrades at low SNR levels, and it may not handle cases where phase ambiguities cannot be distinguished (e.g., when certain conditions in equations are not met).
1：Experimental Design and Method Selection:
The methodology involves dividing the deformed array into subarrays, with the first subarray pre-calibrated. The GESPRIT algorithm is used to estimate phase differences between sensors, and subspace orthogonality along with target movement is leveraged to resolve phase ambiguities.
2：Sample Selection and Data Sources:
A bended linear array with 14 omnidirectional sensors is simulated, with real coordinates provided. Two uncorrelated narrow-band signals from moving targets are used.
3：List of Experimental Equipment and Materials:
No specific physical equipment is mentioned; the study is simulation-based.
4：Experimental Procedures and Operational Workflow:
The array is divided into subarrays (e.g., 5, 5, and 4 sensors). Phase differences are estimated using GESPRIT, and sensor positions are derived. Phase ambiguities are resolved using noise subspace orthogonality and target movement across durations.
5：Data Analysis Methods:
Root mean square error (RMSE) of sensor coordinates is calculated and compared to Cramer–Rao bounds (CRB) through Monte Carlo simulations (200 experiments).
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