Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors
Reads0
Chats0
TLDR
The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor array are considered.Abstract:
Two sensor arrays, hull-mounted array, and towed array sensors are considered for bearings-only tracking. An algorithm is designed to combine the information obtained as bearing (angle) measurements from both sensor arrays to give a better solution. Using data from two different sensor arrays reduces the problem of observability and the observer need not follow the S-maneuver to attain observability of the process. The performance of the fusion algorithm is comparable to that of theoretical Cramer–Rao lower bound and with that of the algorithm when bearing measurements from a single sensor array are considered. Different filters are used for analyzing both algorithms. Monte Carlo runs need to be done to evaluate the performance of algorithms more accurately. Also, the performance of the fusion algorithm is evaluated in terms of solution convergence time.read more
Citations
More filters
Journal ArticleDOI
Underwater target tracking in three-dimensional environment using intelligent sensor technique
Omkar Lakshmi Jagan B.,K. S. +1 more
TL;DR: In this paper , a reliable Unscented Kalman Filter (UKF) algorithm was proposed for enhanced underwater target tracking in a 3D scenario by using bearing, frequency and elevation measurements.
Journal ArticleDOI
Increasing the Uniform Degrees of Freedom for Moving <i>q</i>-Dilated Arrays
TL;DR: In this paper , the number of shifted arrays in the proposed model is shown to be a function of the dilation factor, i.e., dilated arrays with dilation factors with displacements of half wavelength multiples.
Journal ArticleDOI
State Vector’s Fusion for Passive Underwater Tracking Using Two Sensor Arrays
TL;DR: In this article , an algorithm has been developed to detect multiple targets and fuse state vectors when a single target is detected, considering the state vectors from two different sensor arrays with various noise variances.
Journal ArticleDOI
Increasing the Uniform Degrees of Freedom for Moving q-Dilated Arrays
TL;DR: In this article , a new moving array processing model was proposed to estimate the 2-D DOAs, which synthesizes multiple shifted arrays with displacements of half wavelength multiples, and the number of shifted arrays in the proposed model is shown to be a function of the dilation factor.
References
More filters
Proceedings ArticleDOI
New extension of the Kalman filter to nonlinear systems
Simon Julier,Jeffrey Uhlmann +1 more
TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
Proceedings ArticleDOI
The unscented Kalman filter for nonlinear estimation
Eric A. Wan,R. van der Merwe +1 more
TL;DR: The unscented Kalman filter (UKF) as discussed by the authors was proposed by Julier and Uhlman (1997) for nonlinear control problems, including nonlinear system identification, training of neural networks, and dual estimation.
Journal ArticleDOI
Fundamental properties and performance of conventional bearings-only target motion analysis
S. Nardone,A. Lindgren,Kai Gong +2 more
TL;DR: In this paper, the problem of estimating the position and velocity of an object from noise corrupted bearing measurements obtained by a single moving observation platform is considered and a maximum likelihood estimate (MLE) of the target motion analysis solution is developed and its performance analyzed.
Journal ArticleDOI
Kalman Filter Behavior in Bearings-Only Tracking Applications
TL;DR: In this paper, the extended Kalman filter applied to bearings-only target tracking is theoretically analyzed, and closed-form expressions for the state vector and its associated covariance matrix are introduced, and subsequently used to demonstrate how bearing and range estimation errors can interact to cause filter instability.