自动化学报Issue(9)：1867-1874,8.DOI:10.3724/SP.J.1004.2014.01867
基于量化新息的容积粒子滤波融合目标跟踪算法
Target Tracking Algorithm Based on Cubature Particle Filtering Fusion with Quantized Innovation
摘要
Abstract
For the nonlinear networked target tracking system with arbitrarily correlated noises, target tracking fusion algorithms based on quantized innovation and cubature particle filter (CPF) are researched in order to overcome the shortages of the existing methods, which have low precision and poor real-time performance. Firstly, identical transfor-mation of state equation and matrix similarity transformation theory are used for arbitrary noises decorrelation. And then, each sensor node adopts an adaptive quantization strategy to quantize its innovations, and transmits them to the fusion center (FC). Subsequently, the non-Gaussian problems caused by quantization are solved by using CPF to design a fusion tracking algorithm with augmented measurements in the centralized fusion framework. Moreover, its corresponding sequential fusion form is developed. Finally, two computer simulation experiments show the effectiveness of the proposed method.关键词
无线传感器网络/目标跟踪/比特位量化/噪声相关/容积粒子滤波Key words
Wireless sensor networks (WSN)/target tracking/bits quantization/correlated noises/cubature particle filters (CPF)引用本文复制引用
徐小良,汤显峰,葛泉波,管冰蕾..基于量化新息的容积粒子滤波融合目标跟踪算法[J].自动化学报,2014,(9):1867-1874,8.基金项目
国家自然科学基金(601403218,61172133,61273075),浙江省自然科学基金(LQ14F030001),高等学校访问学者专业发展项目(FX2013157)资助Supported by National Natural Science Foundation of China (601403218,61172133,61273075), Natural Science Foundation of Zhejiang Province (LQ14F030001), and Visiting Scholar De-velopment Program for Higher Education Institutions of China (FX2013157) （601403218,61172133,61273075）
