Effective implementation and improvement of fast labeled multi-Bernoulli filter

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.

Multiple-model GLMB filter based on track-before-detect for tracking multiple maneuvering targets

A generalized labeled multi-Bernoulli(GLMB)filter with motion mode label based on the track-before-detect(TBD)strategy for maneuvering targets in sea clutter with heavy tail,in which the transitions of the mode of target motions are modeled by using jump Markovian system(JMS),is presented in this paper.The close-form solution is derived for sequential Monte Carlo implementation of the GLMB filter based on the TBD model.In update,we derive a tractable GLMB density,which preserves the cardinality distribution and first-order moment of the labeled multi-target distribution of interest as well as minimizes the Kullback-Leibler divergence(KLD),to enable the next recursive cycle.The relevant simulation results prove that the proposed multiple-model GLMB-TBD(MM-GLMB-TBD)algorithm based on K-distributed clutter model can improve the detecting and tracking performance in both estimation error and robustness compared with state-of-the-art algorithms for sea clutter background.Additionally,the simulations show that the proposed MM-GLMB-TBD algorithm can accurately output the multitarget trajectories with considerably less computational complexity compared with the adapted dynamic programming based TBD(DP-TBD)algorithm.Meanwhile,the simulation results also indicate that the proposed MM-GLMB-TBD filter slightly outperforms the JMS particle filter based TBD(JMSMeMBer-TBD)filter in estimation error with the basically same computational cost.Finally,the impact of the mismatches on the clutter model and clutter parameter is investigated for the performance of the MM-GLMB-TBD filter.

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian （VB） approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli （LMB） filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density （VB-PHD） filter and the VB cardinality balanced multi-target multi-Bernoulli （VB-CBMeMBer） filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.

A New 4.0-Generation Dendrimer Phosphorescence Labeling Reagent and Its Application to Determination of Trace Alkaline Phosphatase by Affinity Adsorption Solid Substrate-room Temperature Phosphorimetry

A Triton X-100-4.0G-D （4.0G-D refers to a 4.0-generation dendrimer） was brought forward as a new phosphorescence labeling reagent. Two types of specific affinity adsorption （AA） reactions （direct method and sandwich method） were carried out between the labeling product of Triton X-100-4：0G-D-Wheat germ agglutinin （WGA） and alkaline phosphatase （ALP）, the product of AA reaction preserved the good characteristics of room temperature phosphorescence （RTP） of 4.0G-D and △Ip of the product was proportional to the content of ALP. According to the fact stated above, a new method for the determination of trace ALP by affinity adsorption solid substrate-room temperature phosphorimetry （AA-SS-RTP） was established on the basis of WGA labeled with the Triton X-100-4.0G-D. The detection limits were 0.20 ag·spot^-1 （corresponding concentration： 5.0×10^-16 g·mL^-1, namely 5.0×10^-18 mol·L^-1） for a direct method and 0.14 ag·spot^-1 （corresponding concentration： 3.5×10^-16 g·mL^-1, namely 3.5×10^-18 mol·L^-1） for a sandwich method, respectively. For their high sensitivity, good repeatability and high accuracy, the direct method and sandwich method have been successfully appfied to determine the content of ALP in human serum, and the results were coincided with the clinical detection results of the enzyme-linked immunosorbent assay method by the Zhangzhou Hospital of Traditional Chinese Medicine. Meanwhile, the mechanism for the determination of trace ALP by AA-SS-RTP was discussed.

Generalized labeled multi-Bernoulli filter with signal features of unknown emitters

A novel algorithm that combines the generalized labeled multi-Bernoulli(GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features(EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations.Simulation results show that the proposed method can improve the tracking performance of multiple targets,especially in heavy clutter environments.