TY - GEN
T1 - A novel method for extracting road map from the historical measurement set of sensors
AU - Yu, Haojie
AU - Gao, Meiguo
AU - Zheng, Jihong
AU - Wang, Cai
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The road information hidden in the measurements of sensors can be mined to extract the road map and improve the tracking accuracy of the subsequent ground target. In this paper, a novel method which makes full use of the historical measurement set (HMS) of sensors to extract road map is proposed. Firstly, in the case of dense target situations, missing detections and false alarms, the Gaussian mixture probability hypothesis density (GMPHD) filter is a method at choice to estimate the multi-target state, and the multi-target state for a period of time constitutes the multi-target historical state set (MTHSS). Secondly, a density-based clustering method, DBSCAN, is performed on the MTHSS to obtain the road feature points. Finally, the key road nodes are extracted through road feature points by the Douglas-Peukcer (D-P) method. The simulation results show that the proposed method can extract the road map from the HMS of sensors effectively, and the accuracy of the extracted road is sufficient to assist in ground target tracking.
AB - The road information hidden in the measurements of sensors can be mined to extract the road map and improve the tracking accuracy of the subsequent ground target. In this paper, a novel method which makes full use of the historical measurement set (HMS) of sensors to extract road map is proposed. Firstly, in the case of dense target situations, missing detections and false alarms, the Gaussian mixture probability hypothesis density (GMPHD) filter is a method at choice to estimate the multi-target state, and the multi-target state for a period of time constitutes the multi-target historical state set (MTHSS). Secondly, a density-based clustering method, DBSCAN, is performed on the MTHSS to obtain the road feature points. Finally, the key road nodes are extracted through road feature points by the Douglas-Peukcer (D-P) method. The simulation results show that the proposed method can extract the road map from the HMS of sensors effectively, and the accuracy of the extracted road is sufficient to assist in ground target tracking.
KW - Gaussian mixture probability hypotheses density filter
KW - Ground target tracking
KW - Road map extraction
KW - The historical measurement set of sensors
UR - http://www.scopus.com/inward/record.url?scp=85074427497&partnerID=8YFLogxK
U2 - 10.1109/SIPROCESS.2019.8868858
DO - 10.1109/SIPROCESS.2019.8868858
M3 - Conference contribution
AN - SCOPUS:85074427497
T3 - 2019 IEEE 4th International Conference on Signal and Image Processing, ICSIP 2019
SP - 744
EP - 750
BT - 2019 IEEE 4th International Conference on Signal and Image Processing, ICSIP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Signal and Image Processing, ICSIP 2019
Y2 - 19 July 2019 through 21 July 2019
ER -