sensor gain-phase errorestimation is necessary for equipment using sensorarray, such as radar, sonar, and mobile communication before they come into service. Due to this requirement, we propose an offline calibratio...
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sensor gain-phase errorestimation is necessary for equipment using sensorarray, such as radar, sonar, and mobile communication before they come into service. Due to this requirement, we propose an offline calibration algorithm for sensor gain-phase errors using two auxiliary sources, which appear independently of both space and time, named disjoint sources. The significant superiority of this algorithm lies in the use of calibration sources in unknown directions. First, based on the data covariance matrix, the sensor phase errors are obtained, and the relation between phase error matrix and array manifold is established. Second, the directions of two disjoint sources are obtained by the way of 2D search based on eigen-structure subspace method. Third, we provide two methods to realize the algorithm. The proposed algorithm performs independently of phase errors. Moreover, the accurate direction measurement of calibration sources is not necessary. Computer simulations are shown to verify the efficacy of the proposed algorithm.
The presence of sensorarrayerrors due to mutual coupling and channel mismatch among arraysensors severely degrades the performance of direction-of-arrival (DOA) estimation algorithms. This paper proposes a novel se...
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ISBN:
(纸本)9781479958368
The presence of sensorarrayerrors due to mutual coupling and channel mismatch among arraysensors severely degrades the performance of direction-of-arrival (DOA) estimation algorithms. This paper proposes a novel sensorarrayerrors calibration algorithm based on iterative least squares with projection (ILSP) algorithm, which suits for arbitrary array and only needs one auxiliary signal source. This algorithm firstly estimates the true steering vector by using ILSP, then estimates the sensorarrayerrors by solving the equation between the nominal steering vector and the true steering vector. Comparative computer simulation results are presented to illustrate that the proposed algorithm still has lower computational complexity and higher calibration accuracy on the condition of less snapshots and minor DOA intervals of different sampling time.
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