| dc.description.abstract | Short range radars can provide robust information
about their surroundings under atmospheric disturbances,
such as dust, rain, and snow, conditions under which most
other sensing technologies fail. However, this information is
corrupted by received power noise, resulting in false alarms,
missed detections, and range/bearing uncertainty. The reduction
of radar image noise, for human interpretation, as well as
the optimal, automatic detection of objects, has been a focus
of radar processing algorithms for many years. This paper
combines the qualities of the well established binary integration
detection method, which manipulates multiple images to improve
detection within a static scene, and the noise reduction method
of power spectral subtraction. The binary integration method is
able to process multiple radar images to provide probability of
detection estimates, which accompany each power value received
by the radar. The spectral subtraction method then utilizes these
probabilities of detection to form an adaptive estimate of the
received noise power. This noise power is subtracted from the
received power signals, to yield reduced noise radar images.
These are compared with state-of-the-art noise reduction methods
based on the Wiener filter and wavelet denoising techniques. The
presented method exhibits a lower computational complexity than
the benchmark approaches and achieves a higher reduction in
the noise level. All of the methods are applied to real radar data
obtained from a 94-GHz millimetre wave FMCW 2D scanning
radar and to synthetic aperture radar data obtained from a
publicly available data set. | |
