Analyses on Target Tracking under Quantized Communication

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.889-890.658 ◽

2014 ◽

Vol 889-890 ◽

pp. 658-661

Author(s):

Huan Xin Peng ◽

Wen Kai Wang ◽

Bin Liu

Keyword(s):

Kalman Filter ◽

Target Tracking ◽

Tracking System

In the paper, we address the problem of target tracking with quantized communication. While the probabilistic distributed function of the measurement is unknown, in order to simplify the calculations of target tracking system and improve the accuracy of estimation, we propose a Kalman filter under uniformly probabilistic quantization, and analyze the performance of the probabilistic quantization Kalman filter. Simulations results are provided to verify the performance of the target tracking system under uniformly probabilistic quantization.

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Camera-Based Tracking of Floating Objects using Fixed-wing UAVs

Journal of Intelligent & Robotic Systems ◽

10.1007/s10846-021-01432-z ◽

2021 ◽

Vol 102 (4) ◽

Author(s):

Håkon Hagen Helgesen ◽

Torleiv H. Bryne ◽

Erik F. Wilthil ◽

Tor Arne Johansen

Keyword(s):

Kalman Filter ◽

Target Tracking ◽

Covariance Matrix ◽

Extended Kalman Filter ◽

Tracking System ◽

Thermal Camera ◽

Thermal Images ◽

Aerial Vehicle ◽

Floating Objects

AbstractThis article concerns tracking of floating objects using fixed-wing UAVs with a monocular thermal camera. Target tracking from an agile aerial vehicle is challenging because uncertainty in the UAV pose negatively affects the accuracy of the measurements obtained through thermal images. Consequently, the accuracy of the tracking estimates is degraded if navigation uncertainty is neglected. This is especially relevant for the estimated target covariance since inconsistency is a likely consequence. A tracking system based on the Schmidt-Kalman filter is proposed to mitigate navigation uncertainty. Images gathered with an uncertain UAV pose are weighted less than images captured with a reliable pose. The UAV pose is estimated independently in a multiplicative extended Kalman filter where the estimated covariance matrix is a measure of the uncertainty. The method is compared experimentally with two traditional alternatives based on the extended Kalman filter. The results show that the proposed method performs better with respect to consistency and accuracy.

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Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Entropy ◽

10.3390/e21080740 ◽

2019 ◽

Vol 21 (8) ◽

pp. 740 ◽

Cited By ~ 5

Author(s):

Li ◽

Zhao ◽

Yu ◽

Wei

Keyword(s):

Kalman Filter ◽

Target Tracking ◽

Unscented Kalman Filter ◽

Tracking System ◽

Positive Definiteness ◽

Square Root ◽

Tracking Accuracy ◽

Underwater Target ◽

State Observability ◽

Passive Measurements

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.

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