Quick Overview for Multi-target Tracking

Tracking an unknown and time-varying number of targets (e.g., speakers) in indoor environments using audio-visual (AV) modalities has received increasing interest in numerous fields including video conferencing, individual speaker discrimination, and human-computer interaction. The audio-visual sequential Monte Carlo probability hypothesis density (AV-SMC-PHD) filter is a popular baseline for multi-target tracking, offering an elegant framework for fusing audio-visual information and dealing with a varying number of speakers. However, the performance of this filter can be adversely affected by the weight degeneracy problem, where the weights of most of the particles may become very small, while only few remain significant, during the iteration of the algorithm. In this paper, we will short discuss the multi-target tracking.

Tracking by Risky Particle Filtering over Sensor Networks

The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks.

Effect of Resampling on the Performance and Execution Speed of Adaptive Marginalized Particle Filter

One of the major factors that affects the performance of adaptive filters like Particle Filter (PF), Marginalized Particle Filter (MPF) and Adaptive Marginalized Particle Filter (AMPF) is sample degeneracy. Sample degeneracy occurs when the weights associated with particles converges to zero making them useless in state estimation. Resampling is the most common method used to avoid sample degeneracy problem, in which a new set of particles are generated and weights are assigned. Performance and execution time of these filter depends a lot on what type of resampling technique is employed. AMPF is the modified version of MPF which is typically faster than PF and MPF. The main aim of this paper is to find the effect of different types of resampling on the performance and execution time of AMPF. For this, a typical target tracking problem is simulated using MATLAB. AMPF with different types of resampling techniques is used for state estimation for the above-mentioned problem and the best in terms of performance and execution speed will be found out. From the simulation, it will be clear that AMPF with systematic resampling is found to be best in terms of execution speed and performance i.e. minimum Root Mean Square Error.

Binary stars on the galaxy SFH

Using the Yunnan-II evolutionary population synthesis models comprising binary stars, we find that the inclusion of binary stars can raise the derived stellar metallicity Z* and/or age t (degeneracy problem), raise the stellar mass M*, lower the gaseous metallicity Zgas and star formation rate (SFR) of galaxies. This means that a few stars form recently in galaxies, while more stars form during the entire evolution process when considering binary stars. If the degeneracy between t and Z* can be broken, its effect on the feedback process and star formation history can be determined.

Gravitational microlensing I: A unique astrophysical tool

In this paper, we review the astrophysical application of gravitational microlensing. After introducing the history of gravitational lensing, we present the key equations and concept of microlensing. The most frequent microlensing events are single-lens events and historically it has been used for searching dark matter in the form of compact astrophysical halo objects in the Galactic halo. We discuss about the degeneracy problem in the parameters of lens and perturbation effects that can partially break the degeneracy between the lens parameters. The rest of paper is about the astrophysical applications of microlensing. One of the important applications is in the stellar physics by probing the surface of source stars in the high magnification microlensing events. The astrometric and polarimetric observations will be complimentary for probing the atmosphere and stellar spots on the surface of source stars. Finally we discuss about the future projects as space-based telescopes for parallax and astrometry observations of microlensing events. With this project, we would expect to produce a complete stellar and remnant mass function and study the structure of Galaxy in term of distribution of stars along our line of sight towards the center of galaxy.

A Particle Filter Algorithm Based on Mixing of Prior Probability Density and UKF as Generate Importance Function

As an important nonlinear filter theory, particle filter is a heated issue in domestic and foreign researches. The option of importance density is one of the key steps of particle filter algorithm. The proper option of importance density can minish the negative influence of filter algorithm caused by degeneracy problem. This paper introduces several widely-used options of importance density systemically, and analyzes their features and applied perspectives respectively. The paper also advances a comprehensive method of importance density, analyzes its technical features, explores the adjudgement and improvement of this method based on various performance, and finally puts forward the necessary further study according to the engineer requirements.

A Novel Strong Tracking Fault Prognosis Algorithm

Improving the ability to track abruptly changing states and resolving the degeneracy are two difficult problems to particle filter applied to fault prognosis. In this paper, a novel strong tracking fault prognosis algorithm is proposed to settle the above problems. In the proposed algorithm, the artificial immunity algorithm is first introduced to resolve the degeneracy problem, and then the strong tracking filter is introduced to enhance the ability to track abruptly changing states. The particles are updated by strong tracking filter, and better particles are selected by utilizing the artificial immune algorithm to estimate states. As a result, the degeneracy problem is resolved and the accuracy of the proposed fault prognosis algorithm is improved accordingly. The feasibility and validity of the proposed algorithm are demonstrated by the simulation results of the standard validation model and the DTS200 system.