Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 5 Articles
Precise position awareness is a fundamental requirement for advanced applications of emerging intelligent transportation systems,\nsuch as collision warning and speed advisory system. However, the achievable level of positioning accuracy using global navigation\nsatellite systems does not meet the requirements of these applications. Fortunately, cooperative positioning (CP) techniques can\nimprove the performance of positioning in a vehicular ad hoc network (VANET) through sharing the positions between vehicles.\nIn this paper, a novel enhanced CP technique is presented by combining additional range-ultra-wide bandwidth- (UWB-) based\nmeasurements. Furthermore, an adaptive variational Bayesian cubature Kalman filtering (AVBCKF) algorithm is proposed and\nused in the enhanced CP method, which can add robustness to the time-variant measurement noise. Based on analytical and\nexperimental results, the proposed AVBCKF-based CP method outperforms the cubature Kalman filtering- (CKF-) based CP\nmethod and extended Kalman filtering- (EKF-) based CP method....
This paper deals with the design of a fault detection and isolation (FDI) system for an intelligent vehicle, a vehicle equipped with\nadvanced driver assistance system (ADAS). The ADASs are outfitted with sensors for acquiring various information about the\nvehicle and its surroundings. Since these sensors are sensitive to faults, an efficient FDI system should be developed. The designed\nFDI system is comprised of three parts: a detection part, a decision part, and a fault management part. The detection part applies\na generalized observer scheme (GOS). In the GOS, there is bank of extended Kalman filters (EKFs), each excited by all except one\nsensor measurement. The residual generated from the measurement update of each EKF is therefore sensitive to all sensor faults\nbut one. This way, the fault sensitivity pattern of the residual makes it possible to detect a fault and locate the faulty sensor. The\ndesigned FDI system has been implemented and tested off-line with actual experiment data. Good results have been obtained with\ndiagnosing individual sensor faults and outputting fault-free vehicle states....
In order to solve the problem of uncertain noise during the measurement of actual system, an extended Kalman filter fusion\nestimation method based on multisensor fusion algorithm with uncertain effects is proposed. Then the equivalent measurement\nand the corresponding error matrix are estimated by the proposed uncertain fusing algorithm. Submit the results into the system\nmodel for filter processing and the optimal estimation can be obtained by the filtering method. Finally, the algorithm is verified in\nthe GPS/INS navigation system which shows that the fusion result with uncertainty effect is much better than then fusion result\nwith independent noise due to the consideration of correlated noise and uncertain effects for the actual system. This is also validates\nthe effectiveness and practicality of the proposed algorithm....
The demand for navigating pedestrian by using a hand-held mobile device increased remarkably over the past few\nyears, especially in GPS-denied scenario. We propose a new pedestrian dead reckoning (PDR)-based navigation\nalgorithm by using magnetic, angular rate, and gravity (MARG) sensors which are equipped in existing commercial\nsmartphone. Our proposed navigation algorithm consists of step detection, stride length estimation, and heading\nestimation. To eliminate the gauge step errors of the random bouncing motions, we designed a reliable algorithm\nfor step detection. We developed a BP neural network-based stride length estimation algorithm to apply to different\nusers. In response to the challenge of magnetic disturbance, a quaternion-based extended Kalman filter (EKF) is\nintroduced to determine the user's heading direction for each step. The performance of our proposed pedestrian\nnavigation algorithm is verified by using a smartphone in providing accurate, reliable, and continuous location\ntracking services....
At 17:22 UTC on 7th March 2014 Malaysian Airlines flight MH370 carrying 239 passengers\nand crew from Kuala Lumpur to Beijing lost contact with Air Traffic Control and was\nsubsequently reported missing. Over the following days an extensive air and sea search was\nmade around the last reported location of the aircraft in the Gulf of Thailand without success.\nSubsequent analysis of signals transmitted by the aircraft�s satellite communications terminal\nto Inmarsat�s 3F1 Indian Ocean Region satellite indicated that the aircraft continued to fly for\nseveral hours after loss of contact, resulting in the search moving to the southern Indian\nOcean. This paper presents an analysis of the satellite signals that resulted in the change of\nsearch area....
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