Volume 7, Nº 1 (2016)

The paper considers the model of measurement data with typical measurement noise included. For some noise combinations, errors in noise parameter determination are estimated using the Allan variance method. Coefficients of approximating polynomial of Allan variance curve are obtained by multiparametric optimization of the proposed nonlinear target function.

A Pedestrian Integrated Navigation System (PINS) is presented in this paper. PINS includes micro Inertial Measurement Units (IMU), Global Positioning System (GPS) module, magnetometer and barometric altimeter. It is mounted on pedestrian’s waist. When a pedestrian walks, his position, velocity and altitude can be obtained by PINS. The pedestrian gait big or small is detected automatically. According to the information of IMU, GPS, magnetometer and barometric altimeter, Extended Kalman Filter (EKF) is provided. Indoor and outdoor test results show that when the GPS signal is present, the azimuth error is better than 0.5 degrees, the position error is better than 5 meters, and when the GPS signal is absent, the azimuth error is better than 1 degree, the position error is better than 3 percents of the total route, the height error is better than 1 meter and the interior floor can be identified clearly.

The requirements for orientation and stabilization systems of space vehicles are formulated. The state-of-the-art Russian star trackers, inertial sensors, and onboard computer systems intended for orientation and stabilization systems are considered. The problems and prospects for their further development are discussed.

Computer vision hardware and software for navigation and mapping are discussed. The system includes two video cameras. The main features of the software used for calculation and visualization of the library are described in sufficient detail. A method for calibration of a computer vision system using a pair of images with several calibration patterns is presented. The testing equipment for estimation of accuracy in determining navigation parameters with the computer vision system is considered. The results are discussed.

This paper deals with the problem of map-aided navigation. Based on the previous overview of nonlinear filtering algorithms for this problem solution, current trends in the development of such algorithms are discussed. Some new lines in identification of error models and the use of information on the probable vehicle motion are considered.

A finite algorithm for determining a vehicle’s position by differences in measured pseudoranges to known reference points is considered. Equations are derived for the case of excessive number of reference points and for coplanar reference points. A convenient complex-valued form of the problem solution is obtained for the coplanar case.