UAV Array Gain-Phase Error and DOA Joint Estimation
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Solution Overview
Problem
Existing DOA estimation algorithms are hindered by array gain-phase errors, leading to inaccurate or invalid results in practical applications, as they rely on an ideal array manifold that does not account for real-world device and environmental factors.
Innovation Solution
A method for jointly estimating gain-phase errors and DOA using a UAV array, where each UAV is equipped with an antenna, forming a receive array through movement, calculating covariance matrices, and performing eigenvalue decomposition to construct a quadratic optimization problem for joint estimation via spectrum peak search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional DOA estimation algorithms are used with an ideal array manifold, then the algorithm estimation effect is good in theoretical research, but the estimation result has large error or is invalid in practical applications due to array gain-phase errors
Solution Approach 1:
The system uses the UAV array itself to perform self-calibration by estimating the gain-phase errors from the received signals without requiring external calibration sources. The algorithm jointly estimates DOA and gain-phase errors using the covariance matrix and eigenvalue decomposition, allowing the array to correct its own errors autonomously.
Solution Approach 2:
The method incorporates feedback by using the estimated gain-phase errors to correct the array manifold in subsequent DOA estimation iterations. The algorithm repeatedly estimates errors, corrects the manifold, and refines the DOA estimation until convergence, ensuring accurate results despite practical array errors.
2Measurement precision
If auxiliary calibration sources or iterative solutions are used to calibrate gain-phase errors, then calibration accuracy can be improved, but device complexity and operational difficulty increase
Solution Approach 1:
The UAV array performs self-calibration using only the signals it receives from unknown directions. The algorithm extracts gain-phase error information from the signal covariance structure without needing external calibration sources, auxiliary sensors, or complex calibration procedures, thereby maintaining system simplicity while achieving accurate calibration.
Solution Approach 2:
The method extracts gain-phase error information directly from the signal covariance matrix and noise subspace without separating calibration signals from operational signals. This extraction approach eliminates the need for distinct calibration phases or auxiliary calibration sources, simplifying the overall system architecture.
Data Source
AI summary
A method for jointly estimating gain-phase error and direction of arrival (DOA) based on an unmanned aerial vehicle (UAV) array includes: equipping each UAV with an antenna, and forming a receive array through a swarm of multiple UAVs to receive source signals; when an observation baseline of the swarm remains unchanged, changing array manifold through movement of the UAVs, and re-sensing the source signals; for each sensed source signals, calculating a covariance matrix, and obtaining a corresponding noise subspace through eigenvalue decomposition; and constructing a quadratic optimization problem based on the noise subspace and array steering vector, constructing a cost function, and implementing joint estimation of the gain-phase error and the DOA through spectrum peak search. The method can jointly estimate the DOA and gain-phase error and calibrate the gain-phase error, thereby improving accuracy of passive positioning.