The physical causes of clutter and its suppression via sub-band processing

Abstract

Seafloor features that cause low-frequency, active sonar clutter are discussed and illustrated with data gathered in the Mediterranean Sea for signals with a 1 kHz bandwidth, centered on 1.3 kHz. A method is proposed to reduce the number of sonar contacts formed due to random returns in the data, i.e. "clutter points". The method uses band-pass filters to split the signal into a number of sub-bands and processes the sub-band data after contact-forming, using knowledge of the physical causes of clutter to reject clutter while retaining real contacts. Comparisons are made between sub-band and full-band processing and the improvements obtainable via the method are illustrated using the measured data with a shipwreck target. It is shown that the proportion of contacts associated with man-made features is increased when sub-band processing is used, relative to the full-band case.

Clutter‐suppressing performance estimation methods of active sonar waveform based on reverberation statistical models in littoral environment

“…Thus, it is confusing and always leading to false alarms when target speed is also quite small. Similar with the analysis in [3, 4], this paper treats clutter as a component of reverberation, which will change the reverberation characters. Except for the clutter, interference is another factor which increases false alarms.…”

“…Reverberation is the summation of backscattering echoes from scatterers distributed in water surface, seafloor and water volume. As shown in [1, 3, 4], clutters could be defined as a high‐level target‐like output after beamforming and matched filtering, which might arise from large scatterers such as rocks, shipwrecks, seaweeds, fishes and mountains and lead to the formation of false alarms. Commonly speaking, the velocity of scatterers is usually small or nearly zero.…”

“…Clutter factor is formed by false targets, and it is more consistent over frequency. Interference factor is formed by random summation of amplitudes, and it is less consistent over frequency [3]. Clutter is a stationary physical false alarm, while interference is a statistical false alarm and not stationary between ping‐to‐ping.…”

Automatic target classification for low-frequency anti-submarine warfare sonars

“…Some are based on analysis of lowlevel sonar data, such as matched filtered and normalized data [1], [2], [3], [4], [5], [6], while others focus on higher level data such as track information [7], [8], [9]. Also the nature of the classification schemes vary, e. g. pattern recognition [10], inversion [3], track classification [8], spectral characterization [1], [5], principal component analysis [5], [9], and machine learning approaches like decision tree learning [11], various kinds of neural networks [9] and evolutionary approaches [12].…”

“…Sea trials in littoral environments with high reverberant conditions show that high-resolution sonars generate particularly many contacts in presence of ship wrecks and terrain features such as seamounts and underwater ridges [13], [14], [2], [15]. A possible cause is false alarm rate inflation (FARI) [16], [17], [18], [19].…”