Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
Strapdown inertial navigation system/celestial navigation system (SINS/CNS) integrated\nnavigation is a fully autonomous and high precision method, which has been widely used to improve\nthe hitting accuracy and quick reaction capability of near-Earth flight vehicles. The installation errors\nbetween SINS and star sensors have been one of the main factors that restrict the actual accuracy of\nSINS/CNS. In this paper, an integration algorithm based on the star vector observations is derived\nconsidering the star sensor installation error. Then, the star sensor installation error is accurately\nestimated based on Kalman Filtering (KF). Meanwhile, a local observability analysis is performed on\nthe rank of observability matrix obtained via linearization observation equation, and the observable\nconditions are presented and validated. The number of star vectors should be greater than or equal\nto 2, and the times of posture adjustment also should be greater than or equal to 2. Simulations\nindicate that the star sensor installation error could be readily observable based on the maneuvering\ncondition; moreover, the attitude errors of SINS are less than 7 arc-seconds. This analysis method and\nconclusion are useful in the ballistic trajectory design of near-Earth flight vehicles....
A constrained low-cost SINS/OD filter aided with magnetometer is proposed in this paper. The filter is designed to provide a\nland vehicle navigation solution by fusing the measurements of the microelectromechanical systems based inertial measurement\nunit (MEMS IMU), the magnetometer (MAG), and the velocity measurement from odometer (OD). First, accelerometer and\nmagnetometer integrated algorithm is studied to stabilize the attitude angle. Next, a SINS/OD/MAG integrated navigation system\nis designed and simulated, using an adaptive Kalman filter (AKF). It is shown that the accuracy of the integrated navigation system\nwill be implemented to some extent. The field-test shows that the azimuth misalignment angle will diminish to less than 1âË?Ë?. Finally,\nan outliers detection algorithm is studied to estimate the velocity measurement bias of the odometer.The experimental results show\nthe enhancement in restraining observation outliers that improves the precision of the integrated navigation system....
Nowadays, a Global Navigation Satellite System (GNSS) unit is embedded in nearly every smartphone. This unit allows a\nsmartphone to detect the user�s location andmotion, and it makes functions, such as navigation, tracking, and compass applications,\navailable to the user.Therefore, the GNSS unit has become one of the most important features in modern smartphones. However,\nbecausemost smartphones incorporate relatively low-cost GNSS chips, their localization accuracy varies depending on the number\nof accessible GNSS satellites, and it is highly dependent on environmental factors that cause interference such as forests and\nbuildings. This research evaluated the performance of the GNSS units inside two different models of smartphones in determining\npedestrian locations in different environments. The results indicate that the overall performances of the two devices were related\ndirectly to the environment, type of smartphone/GNSS chipset, and the application used to collect the information....
Performance of Global Navigation Satellite System (GNSS) positioning in urban environments is hindered by poor satellite\navailability because there are many man-made and natural objects in urban environments that obstruct satellite signals. To evaluate\nthe availability of GNSS in cities, this paper presents a software simulation of GNSS availability in urban areas using a panoramic\nimage dataset from Google Street View. Photogrammetric image processing techniques are applied to reconstruct fisheye sky view\nimages and detect signal obstacles. Two comparisons of the results fromthe simulation and real world observation in Bangkok and\nTokyo are also presented and discussed for accuracy assessment....
Atime and covariance threshold triggered optimal maneuver planningmethod is proposed for orbital rendezvous using angles-only\nnavigation (AON). In the context of Yamanaka-Ankersen orbital relativemotion equations, the square root unscented Kalman filter\n(SRUKF) AON algorithm is developed to compute the relative state estimations from a low-volume/mass, power saving, and lowcost\noptical/infrared camera�s observations.Multi-impulsiveHill guidance lawis employed in closed-loop linear covariance analysis\nmodel, based on which the quantitative relative position robustness and relative velocity robustness index are defined. By balancing\nfuel consumption, relative position robustness, and relative velocity robustness, we developed a time and covariance threshold\ntriggered two-level optimal maneuver planning method, showing how these results correlate to past methods and missions and\nhow they could potentially influence future ones. Numerical simulation proved that it is feasible to control the spacecraft with\na two-line element- (TLE-) level uncertain, 34.6% of range, initial relative state to a 100m v-bar relative station keeping point, at\nwhere the trajectory dispersion reduces to 3.5% of range, under a 30% data gap per revolution on account of the eclipse. Comparing\nwith the traditional time triggered maneuver planning method, the final relative position accuracy is improved by one order and\nthe relative trajectory robustness and collision probability are obviously improved and reduced, respectively....
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