Advances in Array Signal Processing with Errors: Models, Algorithms and Applications

Dear Colleagues,

Sensor array plays a crucial role in remote sensing. In recent decades, extensive efforts have been devoted to array signal processing. Most of the existing works only consider the ideal signal model, e.g., well-calibrated sensors, approximate signal model, known source number, white Gaussian noise. Unfortunately, errors always exist in actual remote sensing scenarios, for instance, imperfect waveform, gain-phase error, mutual coupling, hybrid-field sources, and coloured noise. In the presence of errors, model mismatch can occur between the practical signal model and the ideal one and yields decreased performance.

This Topic is intended to solicit high-quality contributions in error calibration for remote sensing. Authors are invited to submit original papers presenting new theoretical and/or application-oriented research including models, algorithms, and applications. Additionally, review papers on these topics are also welcome. Topics of interest include but are not limited to:

• Remote sensing with imperfect waveform;
• Remote sensing with advanced signal sampling;
• Remote sensing with sensor position errors;
• Remote sensing with gain-phase errors;
• Remote sensing with sensor mutual coupling;
• Array signal processing with a far-field, near-field, and hybrid-field source;
• Array signal processing with non-Gaussian noise;
• Array signal processing with coherent sources.

Dr. Fangqing Wen
Dr. Wei Liu
Dr. Jin He
Dr. Veerendra Dakulagi
Topic Editors