Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

高等继续教育专业社会适应性评估指标体系构建研究——以国家开放大学为例

顺应经济社会发展和成人学习者需求日趋多元等趋势,高等继续教育领域正在探索包括学科专业建设等在内多层面的"社会适应性"变革。本研究从专业与社会发展的关系出发,对专业社会适应性的概念及评估指标体系的功能做出界定,并从社会发展、学习者需求、学校定位、学科专业自身发展规律等影响专业发展的内外部因素着手,在充分借鉴并参考分析普通本科及高职院校专业适应性相关评估指标的基础上,综合考量高等继续教育专业的特殊性,初步构建了专业社会适应性评估指标体系的基本雏形。之后通过词频分析法及德尔菲法对指标进行确认与修正,利用层次分析法确定评估指标的权重,并结合问卷调查结果对指标进行了信度、效度和相关性检验,最终形成相对科学、合理的专业社会适应性评估指标体系。该体系主要从匹配度、支持度、认可度三个层面来构建,以期指导高等继续教育专业合理发展,促进专业结构优化布局,提高人才培养的适切度,同时为高等继续教育专业设置及专业评估提供参考。