Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filt...Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.展开更多
Canonical quantization is a wonderful procedure for selected problems, but there are many problems for which it fails. Affine quantization is a different procedure that has shown that it can solve many problems that c...Canonical quantization is a wonderful procedure for selected problems, but there are many problems for which it fails. Affine quantization is a different procedure that has shown that it can solve many problems that canonical quantization cannot. Here, words like succeed and fail to refer to whether the quantization results are correct or incorrect. This paper offers two simple examples that serve to introduce affine quantization, and compare studies of two different quantization procedures. Brief comments about field theory and gravity problems undergoing quantization by affine procedures complete the paper.展开更多
基金supported by the National Natural Science Foundation of China(61871301)the Postdoctoral Science Foundation of China(2018M633470,2020T130494)the Fundamental Research Funds for the Central Universities(XJS210211).
文摘Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.
文摘Canonical quantization is a wonderful procedure for selected problems, but there are many problems for which it fails. Affine quantization is a different procedure that has shown that it can solve many problems that canonical quantization cannot. Here, words like succeed and fail to refer to whether the quantization results are correct or incorrect. This paper offers two simple examples that serve to introduce affine quantization, and compare studies of two different quantization procedures. Brief comments about field theory and gravity problems undergoing quantization by affine procedures complete the paper.
