High dynamic global positioning system receiver

A Global Positioning System (GPS) receiver having a number of channels, receives an aggregate of pseudorange code time division modulated signals. The aggregate is converted to baseband and then to digital form for separate processing in the separate channels. A fast fourier transform processor computes the signal energy as a function of Doppler frequency for each correlation lag, and a range and frequency estimator computes estimates of pseudorange, and frequency. Raw estimates from all channels are used to estimate receiver position, velocity, clock offset and clock rate offset in a conventional navigation and control unit, and based on the unit that computes smoothed estimates for the next measurement interval

Improving performance of pedestrian positioning by using vehicular communication signals

Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. In this paper, we propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) Channel state information instead of RSSI is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) Fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80cm when vehicle transmission period is 100ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency

A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements

Multipath propagation causes major impairments to Global Positioning System (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this work, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step

Mathematical specifications of the Onboard Navigation Package (ONPAC) simulator (revision 1)

The mathematical theory of the computational algorithms employed in the onboard navigation package system is described. This system, which simulates an onboard navigation processor, was developed to aid in the design and evaluation of onboard navigation software. The mathematical formulations presented include the factorized UDU(T) form of the extended Kalman filter, the equations of motion of the user satellite, the user clock equations, the observation equations and their partial derivatives, the coodinate transformations, and the matrix decomposition algorithms

Satellite Emission Range Inferred Earth Survey (SERIES) project

The Global Positioning System (GPS) was developed by the Department of Defense primarily for navigation use by the United States Armed Forces. The system will consist of a constellation of 18 operational Navigation Satellite Timing and Ranging (NAVSTAR) satellites by the late 1980's. During the last four years, the Satellite Emission Range Inferred Earth Surveying (SERIES) team at the Jet Propulsion Laboratory (JPL) has developed a novel receiver which is the heart of the SERIES geodetic system designed to use signals broadcast from the GPS. This receiver does not require knowledge of the exact code sequence being transmitted. In addition, when two SERIES receivers are used differentially to determine a baseline, few cm accuracies can be obtained. The initial engineering test phase has been completed for the SERIES Project. Baseline lengths, ranging from 150 meters to 171 kilometers, have been measured with 0.3 cm to 7 cm accuracies. This technology, which is sponsored by the NASA Geodynamics Program, has been developed at JPL to meet the challenge for high precision, cost-effective geodesy, and to complement the mobile Very Long Baseline Interferometry (VLBI) system for Earth surveying

Stand-Alone Satellite-Based Global Positioning

Stand-alone positioning is the first crucial step in the different types of GPS positioning. It is normally adopted only for pseudo-range measurements, after an ionospheric and tropospheric correction, estimated through calculated models. This positioning approach allows estimation of the position of the rover receiver in the ECEF (Earth Centred Earth Fixed) reference system, with a variable level of accuracy which depends on the number of satellites used, but generally it has a metrical level. Stand-alone positioning using pseudo-range measurements will be analysed in this chapter, starting from the actual measurements of a rover receiver. In this chapter, pseudo-range equations will be written and the balance between measurement equations and unknowns (positions and clock offsets) will be analysed. Equations of observation will be linearized in order to be able to solve the problem with the least squares approach, beginning by writing the design matrix. In least squares, it is important to use an adequate stochastic model, in particular some solutions with different weight matrices will be considered. The relative motion defined during the time of signal propagation due to terrestrial rotation and satellite motion is also considered. The iterative procedure devoted to correct the rover position from these effects will also be considered. The least squares solution is followed by the definition of the precision positioning, by means of the estimation of the variance–covariance matrix. Following the theoretical section, a calculus example is proposed, with the purpose of leading the reader to understand the practical positioning problem and to realize an autonomous calculus, verifying the achieved results. Error estimation is not considered here, referring the reader to another chapter. The Dilution of Precision (DOP) is another related topic described in this chapter. This index represents the geometrical quality of satellite constellations. It allows us to foresee the precision of stand-alone positioning, in order to plan the measures. It is derived from the equations regarding stand-alone positioning, where satellite positions are known through the Keplerian elements of the almanac. An example of DOP estimation and visibility of the GPS satellite will be included in this chapter