Title :
Noise-tuning-based hysteretic noisy chaotic neural network for data association in multi-target tracking
Author :
Ming Sun ; Zibo Ma ; Shaojie Cui ; Haiyang, L.V.
Author_Institution :
Coll. of Comput. & Control Eng., Qiqihar Univ., Qiqihar, China
Abstract :
With the help of the noise tuning factor, the noise-tuning-based hysteretic noisy chaotic neural network (NHNCNN) can effectively improve the performance of solutions. In order to improve the accuracy of multi-target tracking, we applied the NHNCNN to calculate the joint association probability of data association in multi-target tracking. Compared to the noisy chaotic neural network (NCNN), the HNCNN is more beneficial to solve the problem of data association. The simulation results indicate that the HNCNN is more effective to improve the accuracy of multi-target tracking.
Keywords :
chaos; neural nets; probability; sensor fusion; target tracking; NHNCNN; data association; joint association probability; multitarget tracking; noise-tuning-based hysteretic noisy chaotic neural network; Clutter; Noise measurement; Optimization; Q measurement; Target tracking; data association; joint association probability; multi-target tracking; noise-tuning-based hysteretic noisy chaotic neural network;
Conference_Titel :
Electronics, Computer and Applications, 2014 IEEE Workshop on
Conference_Location :
Ottawa, ON
DOI :
10.1109/IWECA.2014.6845773
