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Deviation in the multi - sensor information fusion with quasi

Author: ChenShunQian
Tutor: QiGuoQing
School: Nanjing University of Technology and Engineering
Course: Navigation,Guidance and Control
Keywords: Time alignment Spatial Registration Unscented Kalman Filter Incomplete measure
CLC: TP202
Type: Master's thesis
Year: 2010
Downloads: 200
Quote: 1
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Abstract


The deviation in the multi-sensor data fusion alignment is an important branch of the multi- sensor information fusion technology , which by the time of registration and space on each sensor alignment to solve each sensor position error , measurement error and sampling time synchronization causes fusion tracking performance degradation problems. It is a prerequisite for the multi- sensor information fusion of a real-time , high estimation accuracy registration algorithm applicable to a wide range of important engineering practical significance . For this purpose, the registration of the time and space with the prospective research and algorithm improvements . First , for each sensor sampling time is not synchronized , the theoretical basis of Lagrange interpolation and adaptive α-β filtering algorithm , given a real- time registration algorithm simulation to verify the effectiveness of the algorithm . Secondly , for space registration is derived based on the three-dimensional model of the expansion -dimensional particle filter (ASPF) and UCMKF deviation estimation algorithm , and ASEKF ASUKF ASPF and UCMKF bias estimation algorithm algorithm simulation and performance comparison , given the corresponding algorithm to its own characteristics and scope of application. Comparison of various algorithms , higher overall performance to draw ASUKF algorithm in the anti - non-linear , real-time filtering accuracy . Δ sampling points ASUKF large deviation caused by the real-time estimates and reduce the accuracy of , proposed an improved ASUKF deviation registration algorithm , this method can effectively reduce the UT transform dimension to improve the real - time and accuracy . Then study the deviations registration algorithm with feedback information , the purpose of the Act through the fusion of multi-sensor system estimate and the estimation error covariance feedback to each sensor node , each sensor in order to achieve improved target state estimates and bias estimation . The simulation results demonstrate the effectiveness of the algorithm . Finally, the design of the deviation in the incomplete measurements under the conditions of the ASUKF registration algorithm , and the effectiveness of the proposed algorithm is verified by simulation . And draw bias registration error and target state estimation error decreases with the increase of the probability of detection , detection probability is greater than a critical value , the filter is stable conclusion .

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > General issues > Design, performance analysis and synthesis
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