Information fusion in networked underwater target detection

Dong, Yangze; Zhang, Gangqiang; He, Xueming; Tang, Jingjing

doi:10.1109/oceans-genova.2015.7271553

Cited by 6 publications

(3 citation statements)

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“…, and r y (τ) are corresponding acceleration, corresponding speed and corresponding range components of the target concerning observer. The dynamic equation of the target state is given as in Equations (19) and (20).…”

Section: Tracking Using Two Sensor Arraysmentioning

confidence: 99%

“…In [18], the authors applied a data fusion technique using a PF for estimation of robot motions. In [19], the authors analyzed the fusion technique for batch processing using the maximum likelihood estimator and analyzed the same with recursive processing with an unscented Gauss-Helmert Kalman filter. The observability of the system is further discussed in the paper.…”

Section: Introductionmentioning

confidence: 99%

“…It deals with the hardware complexity in target detection. In [19], data fusion was used to detect the target in a properly designed underwater network. In [20], authors used interacting multiple model techniques combined with EKF and UKF for tracking the single as well as multiple targets and compared two different data fusion techniques that involve fusion using nearest neighborhood calculation and fusion using probabilistic data association.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Angle Measurements from Hull Mounted and Towed Array Sensors

Jahan

Rao

2020

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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.

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Section: Tracking Using Two Sensor Arraysmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%