Filter-Type Fault Detection and Exclusion on Multi-Frequency GNSS Receiver
Date Issued
2004
Date
2004
Author(s)
Tsai, Yi-Hsueh
DOI
en-US
Abstract
This thesis is concerned with topics on the problems of satellite fault detection and exclusion (FDE). The purpose of FDE is to detect the presence of unacceptably large positioning error and, further, to exclude the source causing the error, thereby allowing the satellite navigation to continue. To enhance the capability of the existing fault detection and exclusion methods, we propose three type FDE algorithms based on the multi-frequency technique, the autoregressive moving average (ARMA) filter technique and the Kalman filter technique, respectively.
At the first part of this thesis, algorithms using multi-frequency measurements are proposed for GNSS (GPS + Galileo) positioning and FDE. Conventional algorithms adopt only the single frequency L1. However, GPS satellites carrying the L2 and L5 signals for civil use will soon be launched in 2005, and the Galileo system will be fully operational in 2008. Since Galileo will be interoperable with GPS, receivers can be designed to simultaneously access both Galileo and GPS systems. Hence, the number of visible GNSS satellites will be significantly increased. Using the multi-frequency technique can eliminate the ionospheric effect because it is highly related to the carrier frequency of the signal. In addition, the new signals can also be regarded as a backup, and this will significantly increase the safety of navigation. Therefore, application of multi-frequency algorithms will improve the positioning accuracy, shorten the failure detection time, and reduce the incorrect exclusion rate (IER). Simulation results show that, in comparison with the conventional single frequency method, the proposed multi- frequency algorithms not only possess more accurate positioning results but also demonstrate higher performance in detecting and excluding failures.
At the second part of this thesis, we propose an algorithm based on the autoregressive moving average to perform satellite failure detection and exclusion. ARMA filter is widely used in the field of quality control as a tool for fault diagnosis. It uses the historical data as well as the up-to-date information since failure may exist in past measurements before it is detected. The proposed algorithm includes fault detection and fault exclusion. For fault detection, the ARMA-filter is proposed to speed up the detection time by taking the average of the last several sums of the squares of the range residual errors. Speeding up of the failure detection can provide more time for pilots to prevent serious deviations of vehicles from their intended paths. In order to determine the detection threshold under a specified false alarm rate (FAR), the ARMA model is firstly transformed into the state-space model, and the threshold can then be approximated by a “discrete finite-state Markov chain”. Moreover, the alteration of the number of visible satellites will cause problems in data fusion. The probability integral transformation (PIT) method is adopted to solve it. As for fault exclusion, the multivariate ARMA-filter is proposed to reduce the IER by taking the average of the last several parity vectors. Simulation results show that, in comparison with the conventional fault detection methods, the ARMA-filter has higher performance in detecting small failures and however, in detecting large failures, their performances are similar. Moreover, simulation results also verify that the proposed method can reduce the IER in excluding the failed satellite.
At the third part of this thesis, we propose an algorithm based on a parallel bank of Kalman filters to perform satellite positioning and FDE. Conventionally, the well known position- velocity-acceleration (PVA) model is adopted as the dynamic model of Kalman filter for navigation. However, as a moving vehicle accelerates or slows down furiously, or as the vehicle corners at faster speeds, the conventional PVA model without using extra sensors (such as inertial navigation sensors) can no longer be adequate for describing the motion of the vehicle. Therefore, the positioning result of the vehicle will become less accurate. Moreover, the normalized innovation squared (NIS) will deviate from the chi-square distribution and is no longer suitable as the test statistic for FDE. To overcome these problems, the delta range (DR) equation is proposed to accurately model the dynamic behavior of a maneuvering vehicle. Simulation results show that using the proposed DR to replace the PVA model can obtain better positioning and FDE results as the vehicle maneuvers. Furthermore, as a satellite fails at a specified time and if the range measurements associated to the failed one is not yet excluded, the positioning result of the vehicle will become inaccuracy and even unusable. To solve this, an algorithm based on multiple model (MM) approach is proposed. Simulation results also present that, compared to the original Kalman filter, the proposed MM can perform positioning well as the satellite is failed.
Subjects
多頻
GNSS
自我迴歸移動平均
故障偵測與排除
機率積分轉換
卡爾曼濾波器
距離差量
馬可夫鏈
多模型
Kalman filter
Markov chain
multi-frequency
autoregressive moving average
fault detection and exclusion
delta range
multiple model
probability integral transformation
Type
thesis
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