Bearings only target tracking - maneuvering target.
01 Dec 1984-
TL;DR: In this article, the estimation of the position and velocity of a sonar target moving in a two-dimensional frame is studied, where the estimator is a Kalman filter which processes noisy bearings of the target gathered by the tracker.
Abstract: : The estimation of the position and velocity of a sonar target moving in a two-dimensional frame is studied in this paper. The estimator is a Kalman filter which processes noisy bearings of the target gathered by the tracker. The case of maneuvering targets is examined a solution using a variable value of the system's noise covariance matrix is studied. Simulation programs in FORTRAN are provided for a simple example and for maneuvering and nonmaneuvering bearings-only targets. Originator-supplied keywords: Kalman filter, Passive tracking, Bearings-only tracking, and Extended kalman filter.
Citations
More filters
05 Jun 1990
TL;DR: In this paper, a 3D collision avoidance controller for an autonomous underwater vehicle (AUV) that uses a forward-looking high-frequency active sonar is described, where multiple objects are differentiated by clustering sonar returns, and Kalman filters are then used to track both stationary and moving obstacles.
Abstract: A 3D collision avoidance controller for an autonomous underwater vehicle (AUV) that uses a forward-looking high-frequency active sonar is described. Multiple objects are differentiated by clustering sonar returns, and Kalman filters are then used to track both stationary and moving obstacles. A minimum safe distance must be maintained between the AUV and any obstacle, and a modified potential field is used to determine appropriate maneuvers. The merit function which defines this potential field is based on obstacle bearing, distance, and visit count, as well as the AUV heading, the depth, and the direction of the goal. >
17 citations
TL;DR: A TTI estimation scheme which can be used in passive missile warning systems based on Extended Kalman Filter is presented and the algorithm uses the area parameter of the threat plume which is derived from the used image frame.
Abstract: A missile warning system can detect the incoming missile threat(s) and automatically cue the other Electronic Attack
(EA) systems in the suit, such as Directed Infrared Counter Measure (DIRCM) system and/or Counter Measure
Dispensing System (CMDS). Most missile warning systems are currently based on passive sensor technology operating
in either Solar Blind Ultraviolet (SBUV) or Midwave Infrared (MWIR) bands on which there is an intensive emission
from the exhaust plume of the threatening missile. Although passive missile warning systems have some clear
advantages over pulse-Doppler radar (PDR) based active missile warning systems, they show poorer performance in
terms of time-to-impact (TTI) estimation which is critical for optimizing the countermeasures and also “passive kill
assessment”. In this paper, we consider this problem, namely, TTI estimation from passive measurements and present a
TTI estimation scheme which can be used in passive missile warning systems. Our problem formulation is based on
Extended Kalman Filter (EKF). The algorithm uses the area parameter of the threat plume which is derived from the
used image frame.
2 citations
Cites methods from "Bearings only target tracking - man..."
...In [9]-[11], distance estimation was tried to be performed using motion of the target....
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