A Robust Credal Assignment Solution Based on the Generalized Bayes’ Theorem

This paper proposes a new assignment solution based on the Generalized Bayes’ Theorem (GBT) which aims to establish the best matching between two sets of uncertain data. In order to estimate the effectiveness of the proposition, it is compared to the best credal assignment solutions and the well known Global Nearest Neighbor (GNN) algorithm, through synthetic data and a literature example of multi-target tracking scenarios. Given the same input data, the proposed solution gives better assignment results, especially when sensor imprecision increases. However, the proposed solution stills actually computationally more complex than the GNN and the solution proposed by Denoeux et al.

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Optimization of multi-task nodes in wireless sensor networks based on discrete particle swarm algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189813 ◽

2021 ◽

pp. 1-12

Author(s):

Xiuying Zhu

Keyword(s):

Particle Swarm Optimization ◽

Target Tracking ◽

Task Allocation ◽

Nearest Neighbor ◽

Particle Swarm ◽

Wireless Sensor ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle ◽

Multi Target Tracking

Aiming at the competition conflict problem of task allocation of sensor node in wireless sensor network multi-target tracking, a discrete particle swarm optimization tracking task allocation optimization algorithm based on nearest neighbor is proposed. By constructing the mathematical model and objective function of the multi-objective multi-sensor node alliance cooperative tracking task allocation problem, the nearest neighbor method is used to initialize the particle group node task allocation, the objective function is used as the fitness function to guide the particle flight, and the optimal node allocation can be quickly realized. Experiments show that in the case of sparse node coverage, the particle swarm optimization node task allocation method has greatly reduced energy consumption compared with the nearest neighbor method, and can effectively solve the problem of multi-target tracking node task allocation conflict and multiple monitoring alliances on sensor resources the problem of increased system energy consumption during competition conflicts. Discrete particle swarm optimization has superiority for wireless sensor network multi-target tracking in actual environment.

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An Adaptive Real-Time Detection Algorithm for Dim and Small Photoelectric GSO Debris

Sensors ◽

10.3390/s19184026 ◽

2019 ◽

Vol 19 (18) ◽

pp. 4026 ◽

Cited By ~ 1

Author(s):

Quan Sun ◽

Zhaodong Niu ◽

Weihua Wang ◽

Haijing Li ◽

Lang Luo ◽

...

Keyword(s):

Target Tracking ◽

Real Time ◽

Nearest Neighbor ◽

Detection Algorithm ◽

Background Suppression ◽

Main Work ◽

Multi Target Tracking ◽

Real Time Detection ◽

Adaptive Ability ◽

Operating Distance

Geosynchronous orbit (GSO) is the ideal orbit for communication, navigation, meteorology and other satellites, but the space of GSO is limited, and there are still a large number of space debris threatening the safety of spacecraft. Therefore, real-time detection of GSO debris is necessary to avoid collision accidents. Because radar is limited by transmitting power and operating distance, it is difficult to detect GSO debris, so photoelectric detection becomes the mainstream way to detect GSO debris. This paper presents an adaptive real-time detection algorithm for GSO debris in the charge coupled device (CCD) images. The main work is as follows: An image adaptive fast registration algorithm and an enhanced dilation difference algorithm are proposed. Combining with mathematical morphology, threshold segmentation and global nearest neighbor (GNN) multi-target tracking algorithm, the functions of image background suppression, registration, suspected target extraction and multi-target tracking are realized. The processing results of a large number of measured data show that the algorithm can detect dim geostationary earth orbit (GEO) and non-GEO debris in GSO belt stably and efficiently, and the processing speed meets the real-time requirements, with strong adaptive ability, and has high practical application value.

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An Improved Method of Tracking the Motion of Sperm Target

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.241-244.2940 ◽

2012 ◽

Vol 241-244 ◽

pp. 2940-2943

Author(s):

Zhen Rong Deng ◽

Wen Ming Huang ◽

Dong Yu ◽

Yin Wei Li

Keyword(s):

Target Tracking ◽

Nearest Neighbor ◽

Data Association ◽

Improved Method ◽

Tracking Accuracy ◽

Processing Methods ◽

Tracking Method ◽

Multi Target Tracking

This paper analyzes the multi-target tracking of sperm by using the improved nearest neighbor data association tracking method which predicts the position where the sperm may appear in the next frame by computing the interframe-distance and direction of the sperm. Also, in order to dealing with the situation of tracking lose and many-to-one tracking, we proposed two processing methods for sperm re-tracking of tracking lost compensation and tracking forecast minimum-range. The experiment shows this algorithm has better tracking accuracy and higher efficiency.

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