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Micro-Doppler effects of moving vehicles in a radar system are mainly induced by the rotation of wheels, whose features are closely related to the numbers, positions and radiuses of wheels. These parameters of wheels are critical for the vehicle classification and recog¬nition. However, most micro-Doppler features extraction works of vehicles are unable to explicitly extract parame¬ters of wheels. In this paper, a parameter extraction method of vehicle wheels using micro-Doppler features based on the matching pursuit (MP) is proposed. The mi¬cro-Doppler signals of wheels are generally weak compar¬ing to redundant echo signals induced by other irrelevant parts of the vehicle, which makes the micro-Doppler fea¬tures difficult to extract. In this case, several signal atom sets are created according to the motion states of irrele¬vant parts of vehicle and MP is performed to suppress the redundant signals. After the suppression, micro-Doppler signals induced by wheels have become the major part of the echo signal. Another atom set is generated according to the rotational motion of wheels to perform MP again. Then the wheel parameters, such as the estimated numbers, positions and radiuses, are extracted. Simulation results demonstrate that the proposed method is feasible in feature extraction of moving vehicle. Besides, the accuracy can be guaranteed when the signal-to-noise ratio is greater than –5 dB.

Micro-Doppler effects of moving vehicles in a radar system are mainly induced by the rotation of wheels, whose features are closely related to the numbers, positions and radiuses of wheels. These parameters of wheels are critical for the vehicle classification and recog¬nition. However, most micro-Doppler features extraction works of vehicles are unable to explicitly extract parame¬ters of wheels. In this paper, a parameter extraction method of vehicle wheels using micro-Doppler features based on the matching pursuit (MP) is proposed. The mi¬cro-Doppler signals of wheels are generally weak compar¬ing to redundant echo signals induced by other irrelevant parts of the vehicle, which makes the micro-Doppler fea¬tures difficult to extract. In this case, several signal atom sets are created according to the motion states of irrele¬vant parts of vehicle and MP is performed to suppress the redundant signals. After the suppression, micro-Doppler signals induced by wheels have become the major part of the echo signal. Another atom set is generated according to the rotational motion of wheels to perform MP again. Then the wheel parameters, such as the estimated numbers, positions and radiuses, are extracted. Simulation results demonstrate that the proposed method is feasible in feature extraction of moving vehicle. Besides, the accuracy can be guaranteed when the signal-to-noise ratio is greater than –5 dB.

Micro-Doppler effects of moving vehicles in a radar system are mainly induced by the rotation of wheels, whose features are closely related to the numbers, positions and radiuses of wheels. These parameters of wheels are critical for the vehicle classification and recog¬nition. However, most micro-Doppler features extraction works of vehicles are unable to explicitly extract parame¬ters of wheels. In this paper, a parameter extraction method of vehicle wheels using micro-Doppler features based on the matching pursuit (MP) is proposed. The mi¬cro-Doppler signals of wheels are generally weak compar¬ing to redundant echo signals induced by other irrelevant parts of the vehicle, which makes the micro-Doppler fea¬tures difficult to extract. In this case, several signal atom sets are created according to the motion states of irrele¬vant parts of vehicle and MP is performed to suppress the redundant signals. After the suppression, micro-Doppler signals induced by wheels have become the major part of the echo signal. Another atom set is generated according to the rotational motion of wheels to perform MP again. Then the wheel parameters, such as the estimated numbers, positions and radiuses, are extracted. Simulation results demonstrate that the proposed method is feasible in feature extraction of moving vehicle. Besides, the accuracy can be guaranteed when the signal-to-noise ratio is greater than –5 dB.

Micro-Doppler effects of moving vehicles in a radar system are mainly induced by the rotation of wheels, whose features are closely related to the numbers, positions and radiuses of wheels. These parameters of wheels are critical for the vehicle classification and recog¬nition. However, most micro-Doppler features extraction works of vehicles are unable to explicitly extract parame¬ters of wheels. In this paper, a parameter extraction method of vehicle wheels using micro-Doppler features based on the matching pursuit (MP) is proposed. The mi¬cro-Doppler signals of wheels are generally weak compar¬ing to redundant echo signals induced by other irrelevant parts of the vehicle, which makes the micro-Doppler fea¬tures difficult to extract. In this case, several signal atom sets are created according to the motion states of irrele¬vant parts of vehicle and MP is performed to suppress the redundant signals. After the suppression, micro-Doppler signals induced by wheels have become the major part of the echo signal. Another atom set is generated according to the rotational motion of wheels to perform MP again. Then the wheel parameters, such as the estimated numbers, positions and radiuses, are extracted. Simulation results demonstrate that the proposed method is feasible in feature extraction of moving vehicle. Besides, the accuracy can be guaranteed when the signal-to-noise ratio is greater than –5 dB.

This paper investigated the feasibility of the pseudo-static damage identification method derived from a bridge–vehicle interaction system through a moving vehicle laboratory experiment. The element stiffness index, defined as the ratio of flexural rigidity of a damaged member to that of an intact member, serves as the damage indicator. Three vehicle models and two travelling speeds were considered in the experiment to examine the effect of vehicle's dynamic characteristic and travelling speed on identified results. It is demonstrated that locations and severities of damages are detectable using the proposed method in spite of the probable changes of roadway roughness and environmental conditions. In addition, adopting higher vehicle speed as well as the vehicle with frequency close to that of the bridge increased the probability of detecting damages.

Autonomous operation of a vehicle on a road calls for understanding of various events involving the motions of the vehicles in its vicinity. In this paper we show how a moving vehicle which is carrying a camera can estimate the relative motions of nearby vehicles. We present a model for the motion of the observing vehicle, and show how to 'stabilize' it, i.e. to correct the image sequence so that transient motions resulting from bumps, etc. are removed and the sequence corresponds more closely to the sequence that would have been collected if the motion had been smooth. We also model the motions of nearby vehicles and show how to detect their motions relative to the observing vehicle. We present results for several road image sequences which demonstrate the effectiveness of our approach.