Jong-Hwa Song

GPS/DR Error Estimation for Autonomous Vehicle Localization

Autonomous vehicles require highly reliable navigation capabilities. For example, a lane-following method cannot be applied in an intersection without lanes, and since typical lane detection is performed using a straight-line model, errors can occur when the lateral distance is estimated in curved sections due to a model mismatch. Therefore, this paper proposes a localization method that uses GPS/DR error estimation based on a lane detection method with curved lane models, stop line detection, and curve matching in order to improve the performance during waypoint following procedures. The advantage of using the proposed method is that position information can be provided for autonomous driving through intersections, in sections with sharp curves, and in curved sections following a straight section. The proposed method was applied in autonomous vehicles at an experimental site to evaluate its performance, and the results indicate that the positioning achieved accuracy at the sub-meter level.

The stability of the adaptive two-stage Extended Kalman filter

The well-known conventional Kalman filter requires an accurate system model and exact stochastic information. But in a number of situations, the system model has unknown bias, which may degrade the performance of the Kalman filter or may cause the filter to diverge. The effect of unknown bias may be more pronounced on the extended Kalman filter. The two-stage extended Kalman filter (TEKF) with respect to this problem has been receiving considerable attention for a long time. In the case of a random bias, the TEKF assumes that the information of a random bias is known. But the information of a random bias is unknown or partially known in general. To solve this problem, the adaptive two-stage extended Kalman filter (ATEKF) for nonlinear stochastic systems with unknown constant bias or unknown random bias was proposed by Kim and coauthors. This paper analyzes the stability of the ATEKF. To analyze the stability of the ATEKF, this paper shows that firstly the adaptive augmented state extended Kalman filter (ASEKF) is equivalent to the ATEKF and secondly the adaptive ASEKF is uniformly asymptotically stable. The analysis result shows that the upper bound of the error covariance must be appropriately bounded for the filter stability.

The adaptive combined receiver tracking filter design for high dynamic situations

Although the carrier Doppler and the code Doppler are generated by the same relative movement between the satellite and the user, often, the each tracking loop are designed separately and independently. For better GPS signal tracking performance, we need to design the PLL/FLL/DLL altogether optimally. So this paper uses a combined receiver tracking filter, which is the extended Kalman filter to track the C/A code and the carrier frequency together. However this combined receiver tracking filter shows a degraded performance under high dynamic situations because the Doppler frequency changes faster with time. To solve this problem, this paper proposes an adaptive combined receiver tracking filter using an adaptive two-stage extended Kalman filter, which can adapt to an incomplete model and a quickly changed bias. An adaptive combined receiver tracking filter gives a solution for the nonlinear system with the unknown random bias on the assumption that the stochastic information of the random bias is incomplete. The proposed adaptive combined receiver tracking filter has a strong tracking ability to the suddenly changing bias and has acceptable computational complexity. The performance of an adaptive combined receiver tracking filter is verified by simulation.

Performance Enhancement of Land Vehicle Positioning Using Multiple GPS Receivers in an Urban Area

The Global Positioning System (GPS) is the most widely used navigation system in land vehicle applications. In urban areas, the GPS suffers from insufficient signal strength, multipath propagation and non-line-of-sight (NLOS) errors, so it thus becomes difficult to obtain accurate and reliable position information. In this paper, an integration algorithm for multiple receivers is proposed to enhance the positioning performance of GPS for land vehicles in urban areas. The pseudoranges of multiple receivers are integrated based on a tightly coupled approach, and erroneous measurements are detected by testing the closeness of the pseudoranges. In order to fairly compare the pseudoranges, GPS errors and terms arising due to the differences between the positions of the receivers need to be compensated. The double-difference technique is used to eliminate GPS errors in the pseudoranges, and the geometrical distance is corrected by projecting the baseline vector between pairs of receivers. In order to test and analyze the proposed algorithm, an experiment involving live data was performed. The positioning performance of the algorithm was compared with that of the receiver autonomous integrity monitoring (RAIM)-based integration algorithm for multiple receivers. The test results showed that the proposed algorithm yields more accurate position information in urban areas.

Performance Evaluation of Vector Tracking Loop Based Receiver for GPS Anti-Jamming Environment

In this paper, we represent the implementation and performance analysis of vector tracking loop based GPS receiver for jamming environment. The vector tracking loop navigation performance is compared by simulation with conventional tracking loop. The simulation results shows that vector tracking loop is more robust than conventional tracking loop in jamming environment. The vector tracking loop can gain 2dB in jamming performance capability over a conventional GPS receiver. Also, Anti-jamming performance of INS Doppler aiding and deep integration method are compared.

Design of Combined GPS Signal Tracking Loop based on Kalman Filter

The GPS tracking loop consists of three parts in general: discriminator, loop filter and DCO (Digitally Controlled Oscillator). The loop filter is the main part of the tracking loop designed to ensure a good tracking performance. Generally, the loop filter is designed using classical PI(Proportional Integral) control. Although the carrier Doppler and code Doppler are generated by the same relative movement between the satellite and the user, often, the loop filters for each tracking loop are designed separately and independently. Sometimes, they are used in a combined manner such as carrier aided code tracking, FLL assisted PLL, etc. For better GPS signal tracking, we need to design the FLL/PLL/DLL altogether optimally. The purpose of this paper is to design a GPS receiver tracking loop based on the Kalman filter in a combined manner. Also, the proposed GPS receiver tracking loop is compared with a conventional tracking loop in terms of the transfer function and the DCO input. This paper shows that conventional tracking loop is equal to the Kalman filter based tracking loop.