GPS/INS

About: GPS/INS is a research topic. Over the lifetime, 3554 publications have been published within this topic receiving 62784 citations.

Robust innovation-based adaptive Kalman filter for INS/GPS land navigation

Abstract: The integration of Inertial Navigation System (INS) and Global Positioning System (GPS) is a most frequent method for land navigation. Conventional Kalman Filter (CKF) is an optimal estimation algorithm widely used in INS/GPS integration. CKF assumes that the covariance of the system process noise and measurement noise are given and constant. The performance of the CKF degrades seriously, when the GPS measurement noise changes. Researchers introduced an Innovation-based Adaptive Estimation Adaptive Kalman Filter (IAE-AKF) algorithm to keep the filter stable. However, under some extreme condition, the measurement noise may vary tremendously, which will lead to the degradation and divergence of the IAE-AKF. A robust IAE-AKF algorithm is presented in this paper, which evaluates the innovation sequence with Chi-square test and revises the abnormal innovation vector. Simulation and vehicle experiment results show that the new algorithm performs higher accuracy and robustness, and also has the ability to prevent the filtering from being diverged even in a rigorous GPS measurement environment.

An algorithm for combined code and carrier phase based gps positioning

Abstract: The Global Positioning System (GPS) is a satellite based navigation system. GPS satellites transmit signals that allow one to quite accurately estimate the location of GPS receivers. In GPS a typical technique for kinematic position estimation is relative positioning where two receivers are used, one receiver is stationary and its exact position is known, the other is roving and its position is to be estimated. We describe the physical situation and give the mathematical model based on the difference of the measurements at the stationary and roving receivers. The model we consider combines both the code and carrier phase measurements. We then present a recursive least squares approach for position estimation. We take full account of the structure of the problem to make our algorithm efficient, and use orthogonal transformations to ensure numerical reliability of the algorithm. Real data test results suggest our algorithm is effective. An additional benefit of this approach is that the drawbacks of double differencing are avoided. The paper could also serve as a straightforward introduction for numerical analysts to an interesting area of GPS.

Design and Validation of an Accurate GPS Signal and Receiver Truth Model for Comparing Advanced Receiver Processing Techniques

Abstract: : Recent increases in the computational power of computers and digital signal processors have made possible new, novel signal tracking techniques in GPS receivers. One such technique is known as Direct Correlators Output Processing (DCOP). This technique replaces individual traditional tracking loops with a single Kalman Filter, which jointly processes the received signals while exploiting their correlated noises. DCOP is innovative in its potential to replace the tried and true classical signal tracking loops. It is also an enabling technology for ultra-tightly coupled GPS/INS (Global Positioning System/Inertial Navigation System). Potential benefits of these new tracking techniques include an order-of-magnitude improvement in positional accuracy in environments of jamming and high dynamics. However, such performance gains are typically based on software simulations of conceptual GPS receiver designs, not working prototypes. Simulating these new designs requires the modeling of GPS signals and receiver tracking loops, instead of the traditional pseudorange and carrier-phase measurements, which many proven GPS simulation software packages accurately model. The purpose of this research has been to develop an accurate, user-friendly, and customizable GPS signal and receiver model to use for a fair and unbiased evaluation of advanced receiver designs. The result of this research is a Matlab GPS signal simulator, QR a Simulink GPS receiver model implementing true receiver DSP processing, and a Matlab high-speed signal/receiver model that approximates the signal simulator and receiver model.

Ground-multi path mitigation via polarization steering of GPS signal

Abstract: Multipath (MP) is the dominant error source in Global Positioning System (GPS) code-based position solutions requiring high accuracy. A technique is introduced here to mitigate error due to ground-reflected MP signals. The technique uses two orthogonal dipoles to capture the direct GPS signal and the ground-reflected GPS signal. Adjusting the amplitude and phase of the received voltage between the two dipoles can reduce the impact of MP error. Theoretical derivations of this technique are performed for a GPS signal upon reflection from dry soil, seawater, and fresh water. The theoretical results are verified with a real world experiment on the aforementioned surfaces. GPS pseudo-range (PR) and carrier-to-noise ratio (C/No) measurements for specific satellites are used to verify the predicted theoretical results.