Frequency: Quarterly E- ISSN: 2277-629X P- ISSN: Awaited Abstracted/ Indexed in: CNKI Scholar (China National Knowledge Infrastructure), Ulrichâ??s International Periodical Directory, Google Scholar, SCIRUS
Originally apportioned for sea and space navigation, â??Inventi Rapid/Impact: Navigation & Observationâ?? has broadened its scope by including all kinds of navigations e.g. web navigation and robotic navigation inside a human body. Hence, the journal now is interdisciplinary bridging many practice areas including engineering, astronomy, marine sciences, medical sciences and robotics.
Abstract: The 3Cat-3/MOTS (3: Cube, Cat: Catalunya, 3: 3rd CubeSat mission/MissiÃ?³ ObservaciÃ?³\nTerra SatÃ?¨lÃ?·lit) mission is a joint initiative between the Institut CartogrÃ? fic i GeolÃ?²gic de Catalunya\n(ICGC) and the Universitat PolitÃ?¨cnica de Catalunya-BarcelonaTech (UPC) to foster innovative\nEarth Observation (EO) techniques based on data fusion of Global Navigation Satellite Systems\nReflectometry (GNSS-R) and optical payloads. It is based on a 6U CubeSat platform, roughly a 10 cm\nÃ?â?? 20 cm Ã?â?? 30 cm parallelepiped. Since 2012, there has been a fast growing trend to use small satellites,\nespecially nanosatellites, and in particular those following the CubeSat form factor. Small satellites\npossess intrinsic advantages over larger platforms in terms of cost, flexibility, and scalability, and may\nalso enable constellations, trains, federations, or fractionated satellites or payloads based on a large\nnumber of individual satellites at an affordable cost. This work summarizes the mission analysis\nof 3Cat-3/MOTS, including its payload results, power budget (PB), thermal budget (TB), and data\nbudget (DB). This mission analysis is addressed to transform EO data into territorial climate variables\n(soil moisture and land cover change) at the best possible achievable spatio-temporal resolution....
Location awareness is a key enabling feature and fundamental challenge in present and future wireless networks. Most existing\r\nlocalization methods rely on existing infrastructure and thus lack the flexibility and robustness necessary for large ad hoc networks.\r\nIn this paper, we build upon SPAWN (sum-product algorithm over a wireless network), which determines node locations through\r\niterative message passing, but does so at a high computational cost. We compare different message representations for SPAWN\r\nin terms of performance and complexity and investigate several types of cooperation based on censoring. Our results, based on\r\nexperimental data with ultra-wideband (UWB) nodes, indicate that parametric message representation combined with simple\r\ncensoring can give excellent performance at relatively low complexity....
The grid strapdown inertial navigation system (SINS) used in polar navigation also includes\nthree kinds of periodic oscillation errors as common SINS are based on a geographic coordinate\nsystem. Aiming ships which have the external information to conduct a system reset regularly,\nsuppressing the Schuler periodic oscillation is an effective way to enhance navigation accuracy.\nThe Kalman filter based on the grid SINS error model which applies to the ship is established in this\npaper. The errors of grid-level attitude angles can be accurately estimated when the external velocity\ncontains constant error, and then correcting the errors of the grid-level attitude angles through\nfeedback correction can effectively dampen the Schuler periodic oscillation. The simulation results\nshow that with the aid of external reference velocity, the proposed external level damping algorithm\nbased on the Kalman filter can suppress the Schuler periodic oscillation effectively. Compared with\nthe traditional external level damping algorithm based on the damping network, the algorithm\nproposed in this paper can reduce the overshoot errors when the state of grid SINS is switched from\nthe non-damping state to the damping state, and this effectively improves the navigation accuracy of\nthe system....
An attitude estimation algorithm is developed using an adaptive extended Kalman filter for low-cost micro tion algorithm is proposed\nfor fusing triaxial gyroscope and accelerometer measurements. An extended Kalman filter is implemented to estiate attitude in\ndirection cosine matrix (DCM) formation and to calibrate gyroscope biases online.We use a variable measurement covariance for\nacceleration measurements to ensure robustness against temporary non gravitational accelerations, which usually induce errors\nwhen estimating attitude with ordinary algorithms. The proposed algorithm enables accurate gyroscope online calibration by\nusing only a triaxial gyroscope and accelerometer. It outperforms comparable state-of-the-art algorithms in those cases when\nthere are either biases in the gyroscope measurements or large temporary non gravitational accelerations present. A low-cost,\ntemperature-based calibration method is also discussed for initially calibrating gyroscope and acceleration sensors. An open source\nimplementation of the algorithm is also available....
TDDM (time division data modulation) technique will be used in the next generation GNSS (global navigation satellite system)\nto improve processing performance and to reduce inter-GNSS interference; however, the emergence of TDDM signal causes the\nestimation frequency and message reversal fuzz problems in the acquisition process of a GNSS receiver. At present, the traditional\nacquisition methods have some limitations and shortcomings. Therefore, aiming at the unique characteristics of TDDM signal, a\nfast acquisition algorithm is proposed to overcome these fuzz problems in this paper. In the proposed algorithm, three stages are\nobtained by some key technologies, which are the I-Q frequency compensation, superposition processing, subsection processing,\nand reversion position estimation. Besides, the algorithm is simulated from carrier frequency error, code phase error, message\ninversion error, and processing speed. Theoretical and simulation results show that the new algorithm can quickly overcome the\nfuzz problems, and the new algorithm is better than the existing algorithm in the speed and accuracy, which demonstrates that this\nnew algorithm is an effective search scheme for the next generation GNSS signals....
The Kalman filter (KF), which recursively generates a relatively optimal estimate of underlying system state based upon a series of\nobserved measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system\nmodel and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless\ndata set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF) algorithm for the integrated navigation system composed\nof a strapdown inertial navigation system (SINS), a Doppler velocity log (DVL), and a magnetic compass (MCP) is proposed. The\nevolutionary artificial neural networks (EANN) are used in self-learning and training of the intelligent data fusion algorithm. The\nproposed algorithm can significantly out perform the traditional KF in providing estimation continuously with higher accuracy and\nsmoothing the KF outputs when observation data are inaccurate or unavailable for a short period.The experiments of the prototype\nverify the effectiveness of the proposed method....
The European GNSS, Galileo, is currently in its\nin-orbit validation (IOV) phase where four satellites are\nfinally available for computing the user position. In this\nphase, the analysis of the measurements and position\nvelocity and time (PVT) obtained from the IOV satellites\ncan provide insight into the potentialities of the Galileo\nsystem. A methodology is suggested for the analysis of the\nGalileo IOV pseudorange and pseudorange rates collected\nfrom the E1 and E5 frequencies. Several days of data were\ncollected and processed to determine figures of merit such\nas root mean square and maximum errors of the Galileo\nobservables. From the analysis, it emerges that Galileo is\nable to achieve better accuracy than GPS. A thorough\nanalysis of the PVT performance is also carried out using\nbroadcast ephemerides. Galileo and GPS PVTs are compared\nunder similar geometry conditions showing the\npotential of the Galileo system....
Attitude error models play an important role in analyzing the characteristics of navigation\nerror propagation for the design and operation of strapdown inertial navigation systems (SINS).\nHowever, the majority of existing attitude error models focus on misalignment, rather than Euler\nangle errors. Misalignment cannot directly describe attitude error propagation, which is an indirect\nmeasurement. To solve the problem, a general Euler angle error model of SINS is proposed. Based on\nEuler angle error propagation analysis, relative Euler angle errors, and convected Euler angle errors\nare introduced to compose the general Euler angle error model. Simulation experiments are carried\nout to verify the proposed model....
This paper describes the genesis, the principle of operation and characteristics of selected\nradio-navigation positioning systems, which in addition to terrestrial methods formed a\nsystem of navigational marking constituting the primary method for determining the location\nin the sea areas of Poland in the years 1948ââ?¬â??2000, and sometimes even later. The major ones\nare: maritime circular radio beacons (RC), Decca-Navigator System (DNS) and Differential\nGPS (DGPS), as well as solutions forgotten today: AD-2 and SYLEDIS. In this paper, due\nto its limited volume, the authors have omitted the description of the solutions used by the\nPolish Navy (RYM, BRAS, JEMIOÃ?ÂUSZKA, TSIKADA) and the global or continental\nsystems (TRANSIT, GPS, GLONASS, OMEGA, EGNOS, LORAN, CONSOL) - described\nwidely in world literature....
With the rapid development of intelligent transportation systems worldwide, it becomes more important to realize accurate and\nreliable vehicle positioning in various environments whether GPS is available or not. This paper proposes a hybrid intelligent\nmultisensor positioning methodology fusing the information from low-cost sensors including GPS, MEMS-based strapdown\ninertial navigation system (SINS) and electronic compass, and velocity constraint, which can achieve a significant performance\nimprovement over the integration scheme only including GPS and MEMS-based SINS. First, the filter model of SINS aided by\nmultiple sensors is presented in detail and then an improved Kalman filter with sequential measurement-update processing is\ndeveloped to realize the filtering fusion. Further, a least square support vector machine- (LS SVM-) based intelligent module is\ndesigned and augmented with the improved KF to constitute the hybrid positioning system. In case of GPS outages, the LS SVM based\nintelligent module trained recently is used to predict the position error to achieve more accurate positioning performance.\nFinally, the proposed hybrid positioning method is evaluated and compared with traditional methods through real field test data.\nThe experimental results validate the feasibility and effectiveness of the proposed method....
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