Passive Towed Array Shape Estimation Using Heading and Acoustic Data
This paper addresses the matter of array form estimation for passive towed sonar systems during platform maneuvers. Directional noise fields due to distant shipping lanes can be exploited as sources of chance for on-line array shape calibration. In this paper, a nonparametric noise field model is used to form field directionality maps for time-varying array shapes to exploit point and spatially spread sources. This formulation needs neither the quantity nor location of sources in the sector to be known or estimated. Using acoustic knowledge, a most-chance array form estimate is derived where the shape is modeled as a polynomial in heading. Additionally, a methodology for fusing the shape estimate with heading sensor data is introduced. Heading sensors might permanently fail or suffer from high levels of noise throughout turns; so acoustic knowledge will be used to catch up on malfunctioning heading sensors during turns. The combined estimate is filtered using a dynamical model that's valid for sharp turns and accounts for motion of the array perpendicular to tow heading. Multisource simulations are used to demonstrate the performance of the acoustic-primarily based estimate and robustness of the combined estimate.
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