DocumentCode :
3564542
Title :
Association using modified Global Nearest Neighbor in the presence of bias
Author :
Mengzhao Shi ; Qiang Ling ; Zhaohua Yu ; Jin Zhu
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2013
Firstpage :
4688
Lastpage :
4691
Abstract :
Global Nearest Neighbor (GNN) method has been widely implemented in multi-target multi-sensor tracking system, and has achieved a good tracking performance. However, the performance of this approach can be easily destroyed when the sensor biases are involved in the target observations, especially in the cluttered environment with false alarms and missed detections. In order to tackle this issue, this paper proposes a modified GNN method to associate observations and targets in the presence of the sensor bias. Compared with the traditional GNN method, the modified GNN method introduces the new concept of target pattern, and computes the similarity between observations using the feature vector which is constructed using the distance among observations. Simulations show that the modified GNN method has high association success rate, and is robust against the variation of the sensor bias, the number of targets, and the clutter density which the GNN method cannot handle well.
Keywords :
sensor fusion; target tracking; feature vector; global nearest neighbor method; modified GNN method; multitarget multisensor tracking system; sensor bias; target pattern concept; tracking performance; Algorithm design and analysis; Clutter; Indexes; Noise; Radar tracking; Target tracking; Vectors; Feature Vector; Modified GNN; Sensor Bias; Target Pattern;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Type :
conf
Filename :
6640248
Link To Document :
بازگشت