Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 5 Articles
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....
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....
Multipath propagation is one of the major sources of error in GPS measurements. In this research, a ray-tracing technique is\nproposed to study the frequency domain characteristics of multipath propagation.The Doppler frequency difference, also known\nas multipath phase rate and fading frequency, between direct (line-of-sight, LOS) and reflected (non-line-of-sight, NLOS) signals\nis studied as a function of satellite elevation and azimuth, as well as distance between the reflector and the static receiver. The\naccuracy of the method is verified with measured Doppler differences from real data collected in a downtown environment. The\nuse of ray-tracing derived predicted Doppler differences in a receiver, as a means of alleviating the multipath induced errors in the\nmeasurement, is presented and discussed....
Background. Commonmanufactured depth sensors generate depth images that humans normally obtain fromtheir eyes and hands.\nVarious designs converting spatial data into sound have been recently proposed, speculating on their applicability as sensory\nsubstitution devices (SSDs). Objective. We tested such a design as a travel aid in a navigation task. Methods. Our portable device\n(MeloSee) converted 2D array of a depth image into melody in real-time. Distance from the sensor was translated into sound\nintensity, stereo-modulated laterally, and the pitch represented verticality. Twenty-one blindfolded young adults navigated along\nfour different paths during two sessions separated by one-week interval. In some instances, a dual task required them to recognize a\ntemporal pattern applied through a tactile vibrator while they navigated. Results. Participants learnt how to use the system on both\nnew paths and on those they had already navigated from. Based on travel time and errors, performance improved from one week\nto the next. The dual task was achieved successfully, slightly affecting but not preventing effective navigation. Conclusions. The use\nof Kinect-type sensors to implement SSDs is promising, but it is restricted to indoor use and it is inefficient on too short range....
This paper offers a set of novel navigation techniques that rely on the use of inertial sensors and wide-field optical flow information.\nThe aircraft ground velocity and attitude states are estimated with an Unscented Information Filter (UIF) and are evaluated with\nrespect to two sets of experimental flight data collected from an Unmanned Aerial Vehicle (UAV). Two different formulations are\nproposed, a full state formulation including velocity and attitude and a simplified formulation which assumes that the lateral and\nvertical velocity of the aircraft are negligible. An additional state is also considered within each formulation to recover the image\ndistance which can be measured using a laser rangefinder.The results demonstrate that the full state formulation is able to estimate\nthe aircraft ground velocity to within 1.3 m/s of a GPS receiver solution used as reference ââ?¬Å?truthââ?¬Â and regulate attitude angles within\n1.4 degrees standard deviation of error for both sets of flight data....
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