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Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

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Spatial Signature Estimation with an Uncalibrated Uniform Linear Array.

Xiang Cao¹, Jingmin Xin², Yoshifumi Nishio³

¹Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China. cao.xiang@stu.xjtu.edu.cn.

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Summary

This summary is machine-generated.

This study introduces two auto-calibration methods for spatial signature estimation with uniform linear arrays, addressing unknown sensor errors. The techniques accurately determine spatial signatures despite rotational ambiguities in direction-of-arrival estimation.

Keywords:

array signal processing auto-calibration parameter estimation

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Area of Science:

Signal Processing
Array Signal Processing
Electromagnetics

Background:

Spatial signature estimation is crucial for array signal processing.
Uniform linear arrays (ULAs) often suffer from unknown sensor gain and phase errors.
Direction-of-arrival (DOA) estimation in such models is subject to rotational ambiguity.

Purpose of the Study:

To develop novel auto-calibration methods for spatial signature estimation.
To address the challenge of unknown sensor errors in ULAs.
To overcome the rotational ambiguity inherent in ULA DOA estimation.

Main Methods:

Two auto-calibration algorithms are proposed for spatial signature estimation.
Method 1 utilizes subarrays of the ULA, allowing element reuse.
Method 2 exploits array invariances, formulating a multidimensional nonlinear problem solved via Gauss-Newton iteration.

Main Results:

The proposed methods effectively estimate spatial signatures even with unknown sensor errors.
Rotational DOAs and model error parameters are accurately obtained.
The first method provides effective initial values for the second method, enhancing performance.

Conclusions:

The presented auto-calibration techniques offer robust solutions for spatial signature estimation in ULAs.
The methods successfully handle unknown sensor gain and phase errors.
Simulation results validate the effectiveness and accuracy of the proposed algorithms.