Sensor control for multi-object state-space estimation using random finite sets

Automatica ◽  
2010 ◽  
Vol 46 (11) ◽  
pp. 1812-1818 ◽  
Author(s):  
Branko Ristic ◽  
Ba-Ngu Vo
Keyword(s):  
State Space ◽  
Finite Sets ◽  
Object State ◽  
Sensor Control ◽  
Random Finite Sets

scholarly journals Sensor Control in Anti-Submarine Warfare—A Digital Twin and Random Finite Sets Based Approach

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 767 ◽  
Author(s):  
Peng Wang ◽  
Mei Yang ◽  
Yong Peng ◽  
Jiancheng Zhu ◽  
Rusheng Ju ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Real World ◽  
Information Technologies ◽  
Finite Sets ◽  
The Real ◽  
Time Dynamic ◽  
Digital Twin ◽  
Sensor Control ◽  
Random Finite Sets

Since the submarine has become the major threat to maritime security, there is an urgent need to find a more efficient method of anti-submarine warfare (ASW). The digital twin theory is one of the most outstanding information technologies, and has been quite popular in recent years. The most influential change produced by digital twin is the ability to enable real-time dynamic interactions between the simulation world and the real world. Digital twin can be regarded as a paradigm by means of which selected online measurements are dynamically assimilated into the simulation world, with the running simulation model guiding the real world adaptively in reverse. By combining digital twin theory and random finite sets (RFSs) closely, a new framework of sensor control in ASW is proposed. Two key algorithms are proposed for supporting the digital twin-based framework. First, the RFS-based data-assimilation algorithm is proposed for online assimilating the sequence of real-time measurements with detection uncertainty, data association uncertainty, noise, and clutters. Second, the computation of the reward function by using the results of the proposed data-assimilation algorithm is introduced to find the optimal control action. The results of three groups of experiments successfully verify the feasibility and effectiveness of the proposed approach.

scholarly journals Jointly-Optimized Searching and Tracking with Random Finite Sets

2020 ◽  
Vol 19 (10) ◽  
pp. 2374-2391 ◽  
Author(s):  
Savvas Papaioannou ◽  
Panayiotis Kolios ◽  
Theocharis Theocharides ◽  
Christos G. Panayiotou ◽  
Marios M. Polycarpou
Keyword(s):  
Finite Sets ◽  
Random Finite Sets

scholarly journals Poisson Multi-Bernoulli Mixture Trackers: Continuity Through Random Finite Sets of Trajectories

2018 ◽  
Author(s):  
Karl Granstrom ◽  
Lennart Svensson ◽  
Yuxuan Xia ◽  
Jason Williams ◽  
Angel F. Garcia-Femandez
Keyword(s):  
Finite Sets ◽  
Random Finite Sets
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