A new fast algorithm for multitarget tracking in dense clutter

A fast joint probabilistic data association （FJPDA） algorithm is proposed in tiffs paper. Cluster probability matrix is approximately calculated by a new method, whose elements βi^t（K） can be taken as evaluation functions. According to values of βi^t（K）, N events with larger joint probabilities can be searched out as the events with guiding joint probabilities, tiros, the number of searching nodes will be greatly reduced. As a result, this method effectively reduces the calculation load and nnkes it possible to be realized on real-thne, Theoretical ,analysis and Monte Carlo simulation results show that this method is efficient.

Multi-Deme Parallel FGAs-Based Algorithm for Multitarget Tracking

For data association in multisensor and multitarget tracking, a novel parallel algorithm is developed to improve the efficiency and real-time performance of FGAs-based algorithm. One Cluster of Workstation （COW） with Message Passing Interface （MPI） is built. The proposed Multi-Deme Parallel FGA （MDPFGA） is run on the platform. A serial of special MDPFGAs are used to determine the static and the dynamic solutions of generalized m-best S-D assignment problem respectively, as well as target states estimation in track management. Such an assignment-based parallel algorithm is demonstrated on simulated passive sensor track formation and maintenance problem. While illustrating the feasibility of the proposed algorithm in multisensor multitarget tracking, simulation results indicate that the MDPFGAs-based algorithm has greater efficiency and speed than the FGAs-based algorithm.

Multiple model PHD filter for tracking sharply maneuvering targets using recursive RANSAC based adaptive birth estimation

An algorithm to track multiple sharply maneuvering targets without prior knowledge about new target birth is proposed. These targets are capable of achieving sharp maneuvers within a short period of time, such as drones and agile missiles.The probability hypothesis density (PHD) filter, which propagates only the first-order statistical moment of the full target posterior, has been shown to be a computationally efficient solution to multitarget tracking problems. However, the standard PHD filter operates on the single dynamic model and requires prior information about target birth distribution, which leads to many limitations in terms of practical applications. In this paper,we introduce a nonzero mean, white noise turn rate dynamic model and generalize jump Markov systems to multitarget case to accommodate sharply maneuvering dynamics. Moreover, to adaptively estimate newborn targets’information, a measurement-driven method based on the recursive random sampling consensus (RANSAC) algorithm is proposed. Simulation results demonstrate that the proposed method achieves significant improvement in tracking multiple sharply maneuvering targets with adaptive birth estimation.

Multitarget tracking control algorithm under local information selection interaction mechanism

This study focuses on the problem of multitarget tracking.To address the existing problems of current tracking algorithms,as manifested by the time consumption of subgroup separation and the uneven group size of unmanned aerial vehicles(UAVs)for target tracking,a multitarget tracking control algorithm under local information selection interaction is proposed.First,on the basis of location,number,and perceived target information of neighboring UAVs,a temporary leader selection strategy is designed to realize the local follow-up movement of UAVs when the UAVs cannot fully perceive the target.Second,in combination with the basic rules of cluster movement and target information perception factors,distributed control equations are designed to achieve a rapid gathering of UAVs and consistent tracking of multiple targets.Lastly,the simulation experiments are conducted in two-and three-dimensional spaces.Under a certain number of UAVs,clustering speed of the proposed algorithm is less than 3 s,and the equal probability of the UAV subgroup size after group separation is over 78%.

Computer Control Techniques of Phased Array Radars

The computer control techniques applicable to electronically scanned multifunction radars are presented. The software and hardware architecture for the real time control and the data processing within a phased array radar are described. The software system comprising a number of tasks is written in C language and implemented. The results show that the algorithm for the multitask adaptive scheduling and the multitarget data processing is suitable for multifunction phased array radars.

Review:Recent advances in multisensor multitarge11racking using random finite set

In this study,we provide an overview of recent advances in multisensor multitarget tracking based on the random finite set(RFS)approach.The fusion that plays a fundamental role in multisensor filtering is classified into data-level multitarget measurement fusion and estimate-level multitarget density fusion,which share and fuse local measurements and posterior densities between sensors,respectively.Important properties of each fusion rule including the optimality and sub-optimality are presented.In particulax,two robust multitarget density-averaging approaches,arithmetic-and geometric-average fusion,are addressed in detail for various RFSs.Relevant research topics and remaining challenges are highlighted.