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	Reentry guidance based on feedback linearization (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Electronics, Communications and Control, ICECC 2011, September 9, 2011 - September 11, 2011, Ningbo, China Li D.-W. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 This paper presents a new reentry guidance algorithm for RLV (Reusable Launching Vehicle). The algorithm consists of two integrated components: trajectory planning algorithm and tracking algorithm. The most striking feature of algorithm here lies in that both planning and tracking are executed directly in height-velocity space which is different from the methodology of configuration of drag in traditional shuttle guidance. In the session of trajectory planning all trajectory constraints can be expressed with upper bound and lower bound in height-velocity space then a linear interpolation is carried to search the nominal trajectory satisfying the requirement of downrange and target constraints. Then the tracking algorithm uses feedback linearization method to track this nominal profile and meet all constraints. Another typical feature of this algorithm is the strategy of downrange extension using FPA (flight path angle) controller to fulfill the requirement of large downrange. Proper combination of planning-tracking algorithm and FPA controller can bring great flexibility and adaptability to reentry guidance. The algorithm is proved to be robust enough to accommodate the model error and noises in the dynamics. 2011 IEEE.
	A cavity detection method based on machine vision 会议论文 OAI收割 2010 2nd International Conference on Signal Processing Systems (ICSPS 2010), Dalian, China, July 5-7, 2010 作者: Wang ZL(王哲龙); Zhao HY(赵红宇); Junxia Ren; Li HY(李洪谊) 收藏 \| 浏览/下载：22/0 \| 提交时间：2012/06/06 machine vision 3D reconstruction rescue robot profile tracking
	Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China Meng B.; Zhu M.; Han G.; Wu Z. 收藏 \| 浏览/下载：31/0 \| 提交时间：2013/03/25 A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently we first use Sobel algorithm to extract the profile of the object. Then we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.