Optimal Design Based on Closed-Loop Fusion for Velocity Bandwidth Expansion of Optical Target Tracking System

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.

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Optimal Design of Closed-loop Fusion for Sensor Signal Expansion

10.20944/preprints201811.0497.v1 ◽

2018 ◽

Author(s):

Yao Mao ◽

Wei Ren ◽

Yong Luo ◽

ZhiJun Li

Keyword(s):

Optimal Design ◽

Sensor Fusion ◽

Closed Loop ◽

Feedback Compensation ◽

Loop Fusion ◽

Core Issue ◽

Fusion Framework ◽

Compensation Law ◽

Error Optimization

Sensor fusion technology is one of extensive used methods in the field of robot, aerospace and target tracking control. 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. Fusion error optimization problem, which is the core issue of fusion design, is also solved better through the feedback compensation law of CLF framework. Differently from conventional methods, the fusion filter of CLF can be optimally designed and the determination of superposition of fusion information is avoided. To show the validity, simulation and experimental results are to be submitted.

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Closed-Loop Estimation for Randomly Sampled Measurements in Target Tracking System

Mathematical Problems in Engineering ◽

10.1155/2014/315908 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 6

Author(s):

Jin Xue-bo ◽

Lian Xiao-feng ◽

Su Ting-li ◽

Shi Yan ◽

Miao Bei-bei

Keyword(s):

Target Tracking ◽

Dynamic Model ◽

Closed Loop ◽

Tracking System ◽

Estimation Method ◽

Dynamic Parameter ◽

Sampling Interval ◽

Recursive Estimation ◽

Irregular Sampling ◽

Sampled Measurements

Many tracking applications need to deal with the randomly sampled measurements, for which the traditional recursive estimation method may fail. Moreover, getting the accurate dynamic model of the target becomes more difficult. Therefore, it is necessary to update the dynamic model with the real-time information of the tracking system. This paper provides a solution for the target tracking system with randomly sampling measurement. Here, the irregular sampling interval is transformed to a time-varying parameter by calculating the matrix exponential, and the dynamic parameter is estimated by the online estimated state with Yule-Walker method, which is called the closed-loop estimation. The convergence condition of the closed-loop estimation is proved. Simulations and experiments show that the closed-loop estimation method can obtain good estimation performance, even with very high irregular rate of sampling interval, and the developed model has a strong advantage for the long trajectory tracking comparing the other models.

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Properties of neuronal facilitation that improve target tracking in natural pursuit simulations

Journal of The Royal Society Interface ◽

10.1098/rsif.2015.0083 ◽

2015 ◽

Vol 12 (108) ◽

pp. 20150083 ◽

Cited By ~ 8

Author(s):

Zahra M. Bagheri ◽

Steven D. Wiederman ◽

Benjamin S. Cazzolato ◽

Steven Grainger ◽

David C. O'Carroll

Keyword(s):

Target Tracking ◽

Closed Loop ◽

Tracking System ◽

Natural Scenes ◽

Motion Detector ◽

Flying Insects ◽

Electrophysiological Recordings ◽

Competitive Mechanism ◽

Background Clutter ◽

Robotic Applications

Although flying insects have limited visual acuity (approx. 1°) and relatively small brains, many species pursue tiny targets against cluttered backgrounds with high success. Our previous computational model, inspired by electrophysiological recordings from insect ‘small target motion detector’ (STMD) neurons, did not account for several key properties described from the biological system. These include the recent observations of response ‘facilitation’ (a slow build-up of response to targets that move on long, continuous trajectories) and ‘selective attention’, a competitive mechanism that selects one target from alternatives. Here, we present an elaborated STMD-inspired model, implemented in a closed loop target-tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. We test this system against heavily cluttered natural scenes. Inclusion of facilitation not only substantially improves success for even short-duration pursuits, but it also enhances the ability to ‘attend’ to one target in the presence of distracters. Our model predicts optimal facilitation parameters that are static in space and dynamic in time, changing with respect to the amount of background clutter and the intended purpose of the pursuit. Our results provide insights into insect neurophysiology and show the potential of this algorithm for implementation in artificial visual systems and robotic applications.

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