Best hypothesis target tracking and sensor fusion

Author(s):  
Oliver E. Drummond
Author(s):  
Stefan Haag ◽  
Bharanidhar Duraisamy ◽  
Hans-Ludwig Blocher ◽  
Jurgen Dickmann ◽  
Martin Fritzsche ◽  
...  

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 133 ◽  
Author(s):  
Yao Mao ◽  
Wei Ren ◽  
Yong Luo ◽  
Zhijun Li

Micro-electro-mechanical system (MEMS) gyro is one of the extensively used inertia sensors in the field of optical target tracking (OTT). However, velocity closed-loop bandwidth of the OTT system is limited due to the resonance and measurement range issues of MEMS gyro. In this paper, the generalized sensor fusion framework, named the closed-loop fusion (CLF), is analyzed, and the optimal design principle of filter is proposed in detail in order to improve measurement of the bandwidth of MEMS gyro by integrating information of MEMS accelerometers. The fusion error optimization problem, which is the core issue of fusion design, can be solved better through the feedback compensation law of CLF framework and fusion filter optimal design. Differently from conventional methods, the fusion filter of CLF can be simply and accurately designed, and the determination of superposition of fusion information can also be effectively avoided. To show the validity of the proposed method, both sensor fusion simulations and closed-loop experiments of optical target tracking system have yielded excellent results.


2013 ◽  
Vol 13 (1) ◽  
pp. 285-293 ◽  
Author(s):  
Ahmed Dallil ◽  
Mourad Oussalah ◽  
Abdelaziz Ouldali

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3118 ◽  
Author(s):  
Zequn Zhang ◽  
Kun Fu ◽  
Xian Sun ◽  
Wenjuan Ren

In multi-sensor fusion (MSF), the integration of multi-sensor observation data with different observation errors to achieve more accurate positioning of the target has always been a research focus. In this study, a modified ensemble Kalman filter (EnKF) is presented to substitute the traditional Kalman filter (KF) in the multiple hypotheses tracking (MHT) to deal with the high nonlinearity that always shows up in multiple target tracking (MTT) problems. In addition, the multi-source observation data fusion is also realized by using the modified EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF. Numerical studies are given to demonstrate the effectiveness of our proposed method and the results show that the MHT-EnKF method can achieve remarkable enhancement in dealing with nonlinear movement variation and positioning accuracy for MTT problems in MSF scenario.


Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1255 ◽  
Author(s):  
Jie Zhou ◽  
Yan Liang ◽  
Qiang Shen ◽  
Xiaoxue Feng ◽  
Quan Pan

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