Sonar based automatic target detection scheme for underwater environments using CFAR techniques: a comparative study

Abstract

Absrracr-Asonar detection scheme is presented here using Constant False Alarm Rate (CFAR) techniques. The proposed system has the following advantage. The technique yields unbiased estimates under a non-homogenous underwater environment, because the false alarm rate is maintained at a constant level while lbe threshold changes with difierent underwater environments In presence of interfering targets. A mathematical analysis is also being done for difierent CFAR processors with the interesl on the CFAR losses. In this paper, average decision threshold (ADT) is used as a measure.Index Te-CFAR, Sonar target detection scheme LINTRODUCTION An acoustic communication system, particularly one functioning in an environment with an autonomous underwater vehicle (AW), must contend with a number of man-made interference sources in addition to the more familiar ambient noise sources such as Biological noise etc. Thruster and aCNator noise often contains tonal components and therefore introduces narrow band interference to a communications receiver on board the vehicle 171. In active sonar systems, detection is the process of deciding if a target echo is present in a return. This is often accomplished by an operator who examines the sonar receiver output and decides if any portion of it is larger than the expected response due to reverberation. If so, then a target is declared to be present at the range and bearing associated with the peak in the receiver output. A typical sonar return is as shown in Fig. 1. As the receiver possesses a fixed range resolution, its output can be divided into cells of equal width in range as shown in Fig. 2.Detection may also be accomplished automatically using a processor that sets a threshold based on a sampling of the receiver output without an operator in the loop. Commonly, such processors are designed to maintain a constant probability of false alarm per display resolution cell, which is usually between IO4 to IO" and the detection probability for a single scan of the target must be on the order of 0.5 to 0.9 at maximum range [3]. Such processors use samples of the receiver output to implicitly estimate the parameters of its statistical model and appropriately set a detection threshold. The class of processors used to accomplish automatic detection is referred to as Constant False Alarm Rate (CFAR) detection processors which set the threshold adaptively based on local information of the noise power. CFAR processors are useful for detecting targets in background for 0-7803-8541 -1/04/$20.00 02004 IEEE which all parameters in the statistical distribution are not known and may be non stationary. The signal r e h " from targets are usually buried in noise and clutter, which refers to any undesired signal echo that is reflected back to the receiver. Since the clutter-plus-noise power is not known at any given location, a fixed threshold detection scheme cannot be applied to the sonar returns in individual range cells if the false alarm rate is to be controlled.

The Establishment of Intelligent Detection Method and Monitoring System for Underwater Target Based on Imaging Sonar

“…Median filter and threshold segmentation are mainly be used to remove the noise in the image [5][6][7][8]. Filter is mainly used to remove isolated noise point, and median filter can better keep the edge character of image, which is helpful to extract target and calculate related properties.…”

Development of Underwater Low-speed Moving Target Detection Using Spatiotemporal Variance of Phase Difference for Port Surveillance Sonar

“…The typical methods used for reducing reverberation and echoes of static objects using the signal amplitude are background subtraction 9) , normalization using a median filter, and constant false alarm rate (CFAR) 10) . For the amplitudebased method, background subtraction is used since it efficiently removes undesirable signals.…”

Clutter map detector for active diver detection sonar

“…The Rayleigh reverberation results in a target model, where the signal amplitude squared is exponentially distributed. These assumptions lead to the classical constant false alarm rate (CFAR) detectors in radar and sonar systems [18–20]. These detectors need to estimate the mean of the fluctuations in the neighbourhood of the pixel under test.…”