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”Sparse microwave can improve imaging performance”

China: Sparse microwave imaging is believed to have the ability to resolve the conflict between growing microwave imaging performance requirements and increasing system complexity, observed Chinese researchers. Under this new microwave imaging concept, the system complexity could be reduced remarkably without adversely affecting the imaging performance.

Sparse microwave imaging theory and technology can be applied in two ways: to design new systems, and to improve existing microwave imaging devices. As a new microwave imaging concept, one can design optimised microwave imaging systems using sparse microwave imaging theory for guidance. One can also use the signal processing methods of sparse microwave imaging to improve the imaging performance of the existing microwave devices, e.g. to increase the image distinguishability, reduce the sidelobes and reduce ambiguity.

Under the support of the 973 programme, “Study of theory, system and methodology of sparse microwave imaging”, Chinese scientists have conducted considerable research into most aspects of sparse microwave imaging, including its fundamental theories, system design, performance evaluation and applications. Their work, consisting of a series of papers, has been published in Science China Information Sciences.

Microwave imaging is one of the two major tools of remote sensing, and has been widely used in fields such as agriculture, forestry, oceanic monitoring, topography mapping and military reconnaissance. The best known modern microwave imaging technology used in remote sensing is synthetic aperture radar (SAR), which transmits an electromagnetic wave toward the scene from a platform moving in a straight line, receives the radar echo and produces a high resolution microwave image via signal processing.

Compared with optical sensing, microwave imaging has the ability to provide all-weather round-the-clock observation, and can be applied to deal with some special sensing requirements, including moving target detection and digital elevation model extraction.

As microwave imaging technology has been used in increasing numbers of fields, the users have of course raised demands for numerous new requirements for their microwave imaging systems. Among them, high resolution and a wide mapping swath are the basic requirements for modern microwave imaging systems. High resolution means that more details can be observed, and the wide mapping swath means larger observation areas.

The concept of sparse microwave imaging was developed to improve signal bandwidth and sampling rate. Sparse microwave imaging introduces sparse signal processing theory to microwave imaging as a replacement for conventional signal processing schemes based on matched filtering. Sparse signal processing was a concept that was developed by mathematicians in the late 1990s, and includes a set of mathematical tools designed to deal with sparse signals – a signal is sparse when most of the elements of the signal are (or are very close to) zero.

Thanks to the extraordinary work known as compressive sensing by D. Donoho, E. Candes and T. Tao over the last decade, sparse signal processing theory, and compressive sensing theory in particular, has become a focal point for research in current signal processing fields. Essentially, sparse signal processing theory asserts that, if a signal is sparse, then it can be measured with far fewer samples than would be required for traditional sampling schemes, and can then be perfectly reconstructed from these few samples via sparse reconstruction algorithms.

The researchers concluded by saying, that although there are many problems with the technology that need to be solved, sparse microwave imaging can be expected to have a bright future.

Source: Science Newsline