Predicting Sonar False Alarm Rate Inflation Using Acoustic Modeling and a High-Resolution Terrain Model

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

“…For each track update in each instance the probability that the track update is generated due to FARI is estimated using the method described by Hjelmervik [7]. The FARI values are averaged over all track updates for each instance.…”

“…The steps described in [7] are taken to estimate a probability of FARI for each track update in an instance. The probability that a track update at position (θ, r) is generated due to FARI is given by:…”

“…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].…”

“…Tracks based on contacts due to FARI are typically stationary and appear close to strong uslopes or sea mounts [7]. These properties may be exploited for classification purposes.…”

“…With accurate knowledge of the environmental conditions (detailed topography and an accurate sound speed profile) an acoustic model may estimate the reverberation received on the sonar. This may in turn be used to estimate the probability of FARI [7]. Furthermore, since the tracks are stationary, then the track kinematics may be used to determine if a track is due to FARI or on an actual target [8].…”