基于神经网络的变结构多模型减少模式集跳变延迟

信号处理 Pub Date : 2001-08-06 DOI:10.1109/SSP.2001.955242

D. Choi, B. Ahn, Hanseok Ko

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引用次数: 1

摘要

变结构多模型(VSMM)是有效跟踪单个机动目标最强大的算法之一。虽然VSMM是专门为改进以降低计算成本和提高跟踪性能为重点的交互式多模型(MM)方法而开发的，但它存在固有的局限性，即模式集跳变延迟(MJD)的存在。对VSMM中的MJD现象进行了描述和分析。为了消除MJD，提出了一种基于神经网络的VSMM算法，该算法根据监督训练的结果自动选择最优模式集。通过典型的仿真，我们证明了该算法在跟踪误差方面优于传统的有向图切换VSMM。

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Neural net based variable structure multiple model reducing mode set jump delay

Variable structure multiple model (VSMM) is one of the most powerful algorithms for effectively tracking a single maneuvering target. Although VSMM is developed specifically to improve the interactive multiple model (MM) method focused to reducing computational cost and improving tracking performance, it presents an inherent limitation in the form of the presence of mode set jump delay (MJD). MJD as an undesirable phenomenon in VSMM is described and analyzed. In order to eliminate the MJD, a neural network based VSMM that automatically selects the optimal mode set as achieved by supervised training is proposed. Through representative simulations we show the proposed algorithm outperforming over the conventional digraph switching VSMM in terms of tracking error.

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来源期刊

信号处理

自引率

0.00%

发文量

5812

期刊介绍： Journal of Signal Processing is an academic journal supervised by China Association for Science and Technology and sponsored by China Institute of Electronics. The journal is an academic journal that reflects the latest research results and technological progress in the field of signal processing and related disciplines. It covers academic papers and review articles on new theories, new ideas, and new technologies in the field of signal processing. The journal aims to provide a platform for academic exchanges for scientific researchers and engineering and technical personnel engaged in basic research and applied research in signal processing, thereby promoting the development of information science and technology. At present, the journal has been included in the three major domestic core journal databases "China Science Citation Database (CSCD), China Science and Technology Core Journals (CSTPCD), Chinese Core Journals Overview" and Coaj. It is also included in many foreign databases such as Scopus, CSA, EBSCO host, INSPEC, JST, etc.