A Multi-hypothesis Filter for Passive Tracking of Surface and Sub-Surface Target Localization

Abstract

A Multi-hypothesis Iterated-Extended Kalman Filter (MHEKF) for passive sonar tracking and localization of surface and submerged targets using elevation and bearing angle measurements is presented. The MHEKF operates in a multi-depth mode by creating a bank of independently operating range-parameterized Cartesian EKFs, each receiving the same measurement data. The multi-depth mode operation allows the EKF to determine a unique (x, y, z) position solution using elevation and bearing measurements. At the first available measurement, the multi-hypothesis filter logic calculates the number and positioning of the depth banks in the water column from the operational decision radius along with the sensor beam widths and water depth. The bank depths are set in a geometric progression that yields constant coefficient of variation in range calculated with respect to the operational decision region. The MHEKF uses the normalized likelihood from each EKF depth-mode output to recursively update the target track. Any velocity or Doppler information available will reduce track ambiguity that arises when the tracker is expected to distinguish fast moving targets on the surface from slow moving targets at depth.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA499747

Entities

People

  • C. Sermarini
  • G. Dobeck
  • J. Hyland
  • J. Wilbur

Organizations

  • Naval Surface Warfare Center

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Analysis Of Variance
  • Cartesian Coordinates
  • Coordinate Systems
  • Covariance
  • Detectors
  • Elevation
  • Equations
  • Error Analysis
  • Errors
  • Filters
  • Intervals
  • Kalman Filters
  • Multiple Hypothesis Tracking
  • Passive Tracking
  • Surface Targets
  • Target Tracking
  • Targets

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Radar Systems Engineering.
  • Sensor Fusion and Tracking Systems.