Design and Processing of Multi-Dimensional Signals

Dear Colleagues,

The design and processing of multi-dimensional signals have sparked intense interest from the research community. In this respect, various requirements in systems, such as sensor, radio, ultrasound, sonar, speech, holographic, and photographic systems, call for elaborate design of the multi-dimensional signal, which also expedite the theoretical developments in multi-dimensional signal processing. Capitalizing the acquisition of information in multiple dimensions, a host of applications, ranging from remote sensing, communication systems to internet of things, can benefit from multi-dimensional signal processing, where different signal characteristics are properly fused to gain complete knowledge in a given application field.

Concepts in multi-dimensional signal design and processing are mathematically derived from one-dimensional signal design and processing with several twists, turns, and spreads. Specifically, some basic processing techniques, such as the Parseval’s theorem, Fourier transform, Z transform, and Nyquist’s Sampling theorem, have been developed, where a thorough understanding of the spectral properties of the signal and the input-to-output transfer properties of the system are required. Moreover, novel approaches are appearing, such as machine learning, deep learning, convolutional neural networks, and dynamic complex networks approaches. The complicated electromagnetic environment has forced the signal processing community to face new challenges such as different kinds of interferences and clutter. Furthermore, the advent and widespread use of multi-sensor systems has substantially increased the processing burden, so new processing tools are encouraged in order to deal with such the big data problem.

The Special Issue aims to showcase a variety of the topics, ranging from fundamentals to important new findings, which are currently investigated with design and processing of multi-dimensional signal. The types of signals presented in this Issue range from radar signals, statistical signals, high-speed real-time signals, and image signals, and include improved classical tools such as feature extraction, data fusion, sparse representation, compressive sensing, cognitive sensing, and learning methods. Furthermore, some novel signal design tools, including waveform diverse array technology and waveform design and optimization, will be presented. The latest technological development and academic research into hot issues will be collected, which will cover the aspects of civil or defense applications, such as target detection/tracking/recognition, interference suppression, anti-jamming, and space–time adaptive processing for clutter suppression. Topics include, but are not limited to, the following:

• Radar signal processing
• Statistical signal and array processing
• High-speed real-time signal processing
• Image processing
• Multi-dimension feature extraction and data fusion
• Sparse representation and compressive sensing
• Waveform design and optimization
• Space–time adaptive processing
• Waveform diverse array technology
• Cognitive sensing and learning methods
• Target detection/tracking/recognition
• Interference suppression and anti-jamming

Prof. Dr. Guisheng Liao
Dr. Lan Lan
Guest Editors

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• array processing
• radar signal processing
• waveform diverse array
• waveform design
• adaptive beamforming
• signal, image, and video coding
• machine learning
• deep learning