Amplitude-phase error correction and DOA estimation method based on convex optimization

Abstract

The invention belongs to the field of parameter estimation in array signal processing, and specifically relates to an amplitude-phase error correction and DOA estimation method based on convex optimization. The method comprises the following steps: (1), carrying out the DOA estimation of the data with the amplitude-phase error interference; (2), substituting an estimated angle into a constructed optimization model to obtain an estimated amplitude-phase error; (3), performing the iteration of the former two steps till convergence to obtain the estimated DOA and the amplitude-phase error; (4), carrying out the mesh refinement of the estimated angle, so as to reduce the network incompatibility influence at step (1). Aiming at the phenomenon that the performance of an antenna array will be affected by environmental factors, the method is employed for the signal data received by the antenna array, achieves the blind correction of the amplitude-phase error of a uniform array, and improves the DOA estimation accuracy. The method can achieve the angle estimation of a coherent signal source, does not need an auxiliary signal source and an auxiliary array element, has certain universality, and is especially suitable for the amplitude-phase error correction under the condition that the coherence of the signal is not known.

Description

Technical field [0001] The invention belongs to the field of parameter estimation in array signal processing, and specifically relates to a method for amplitude and phase error correction and DOA estimation based on convex optimization. Background technique [0002] DOA estimation has a wide range of applications in radar, sonar and wireless communications. However, in actual applications, due to the interference of environmental temperature, humidity, etc., the gain of the antenna array often changes inconsistently, which makes the measurement results have large deviations and causes the measurement results to be inaccurate. There are already some DOA estimation methods for the presence of amplitude and phase errors, such as the covariance matrix least square method, the maximum likelihood method based on subspace orthogonality, the use of auxiliary array elements, and the source correction method. However, these methods have certain defects and shortcomings. For example, the s...

AI Technical Summary

Problems solved by technology

The maximum likelihood algorithm is another important research content in super-resolution DOA estimation. Its estimation accuracy is higher, but its optimal solution usually requires the use of very complex multi-dimensional nonlinear search techniques, resulting in a very large amount of calculation. Real-time processing is not possible with the current hardware level

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