Measurement Science and Technology Meas. Sci. Technol. 28 (2017) 055102 (12pp)
https://doi.org/10.1088/1361-6501/aa5c66
Integrity monitoring of vehicle positioning in urban environment using RTK-GNSS, IMU and speedometer Ahmed El-Mowafy1 and Nobuaki Kubo2 1
Department of Spatial Sciences, Curtin University, GPO Box U 1987, Perth, WA 6845, Australia Tokyo University of Marine Science and Technology, 2-1-6 etchujima Koto-ku, Tokyo, Japan
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E-mail:
[email protected] Received 1 November 2016, revised 19 January 2017 Accepted for publication 26 January 2017 Published 3 March 2017 Abstract
Continuous and trustworthy positioning is a critical capability for advanced driver assistance systems (ADAS). To achieve continuous positioning, methods such as global navigation satellite systems real-time kinematic (RTK), Doppler-based positioning, and positioning using low-cost inertial measurement unit (IMU) with car speedometer data are combined in this study. To ensure reliable positioning, the system should have integrity monitoring above a certain level, such as 99%. Achieving this level when combining different types of measurements that have different characteristics and different types of errors is a challenge. In this study, a novel integrity monitoring approach is presented for the proposed integrated system. A threat model of the measurements of the system components is discussed, which includes both the nominal performance and possible fault modes. A new protection level is presented to bound the maximum directional position error. The proposed approach was evaluated through a kinematic test in an urban area in Japan with a focus on horizontal positioning. Test results show that by integrating RTK, Doppler with IMU/speedometer, 100% positioning availability was achieved. The integrity monitoring availability was assessed and found to meet the target value where the position errors were bounded by the protection level, which was also less than an alert level, indicating the effectiveness of the proposed approach. Keywords: integrity monitoring, RTK, GNSS, speedometer (Some figures may appear in colour only in the online journal)
1. Introduction
currently under testing by car manufacturers for ADAS applications. However, RTK positioning may not be possible when there is a temporary break in receiving the reference station data or when working in an urban environment due to GNSS signal blockage (El-Mowafy 2000). To bridge positioning during these breaks other sensors such inertial measurement units (IMUs) can be used (Godhaa and Cannon 2007, Yand et al 2014, Yang et al 2016). However, low-cost IMUs that are affordable for car applications generate a significant heading bias, which increases with time. Such a bias can be corrected by the heading information obtained from GNSS Doppler measurements when they are available. Moreover, Doppler measurements can be used for measuring position changes with time. A drawback of these methods is that positioning
Advanced driver assistance systems (ADAS) with features such as lane change assist and automatic braking have experienced a rapid growth and within a few years the driverless cars will be commercially available. These advanced systems require knowledge of the accurate vehicle location in real time, which can be achieved by integrating multiple positioning technologies that have different types of measurements with different types of errors. For instance, real-time kinematic (RTK) positioning using global navigation satellite systems (GNSS) observations can provide less than 5 cm accuracy. With the rapid decrease of the hardware cost and the development of economy single-frequency systems, the use of RTK is 1361-6501/17/055102+12$33.00
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© 2017 IOP Publishing Ltd Printed in the UK
A El-Mowafy and N Kubo
Meas. Sci. Technol. 28 (2017) 055102
errors increase with time; therefore, they are only used for bridging short breaks in RTK positioning. Since positioning is a critical function in ADAS, high degree of positioning reliability is needed. Therefore, the positioning system should be able to detect and isolate measurement faults, bound possible measurement errors, and raises an alarm in the event of unreliable positioning performance. This capability is referred to as integrity monitoring (IM). Furthermore, when no malfunction is detected, IM provides a protection level (PL) that bounds the true position error with a certain probability of risk (El-Mowafy 2016). Methods such as advanced receiver autonomous integrity monitoring (ARAIM) have been developed for IM in aviation applications mainly employing code observations by integration of several GNSS constellations (Blanch et al 2013, 2014, Rippl et al 2014, El-Mowafy and Yang 2016). However, little research has been done for applications that utilise phase and code data. For example, Azaola-Sáenza and Cosmen (2009) and Cezón et al (2013) presented an isotropy-based protection level technique (IBPL) for integrity monitoring focusing on precise point positioning (PPP). Merino and Lainez (2012) and Jokinen et al (2013) discussed similar approaches. Furthermore, IM in the presence of phase cycle ambiguities was discussed in Khanafesh and Pervan (2010) and Khanafesh and Langel (2011). While most IM and ARAIM studies are focused on developing methods for aviation using only GNSS measurements, very little research was presented for IM when integrating GNSS with other sensors or when being used for car applications. Studies such as Belabbas and Grosch (2013) showed IM when integrating GNSS with IMU but for train navigation. In this contribution, we present a new method for integrity monitoring of positioning when integrating RTK with lowcost IMU, vehicle speed sensors and Doppler measurements for advanced automotive applications. We present an ARAIM method that uses both phase and code data in RTK. We restrict our focus to horizontal positioning, which is the key positioning component needed for ADAS applications. The next section describes the positioning approaches used in this study. Section 3 presents the proposed integrity monitoring method for the assimilated system. In section 4, testing of the proposed methods is described and test results are presented and analysed.
satellites should be observed with a suitable geometry when using a single GNSS system and a minimum of two satellites are needed for each additional system. Hence, when the number of observed satellites drop to four for a single GNSS system, for example due to signal blockage, time-integrated GNSS Doppler-based velocity can be used to determine changes in position (Misra and Enge 2006). These changes in position when being added to an initial position, determined for instance from RTK, can bridge short RTK positioning gaps. Positioning in this mode will be dependent on satellite geometry and reliability in measuring the speed of the vehicle. Typically, accuracy of Doppler measurements is related to the speed, where when the vehicle stops, Doppler observations cannot be directly used for estimation of velocity as the extracted information would represent noise. Hence, to avoid synthetic jumps in the estimated velocity and to limit positioning errors to under 1 m after one minute of continuous accumulation of the Doppler-measurements, the following two conditions should be met: (i) good satellite geometry, defined by a horizontal dilution of precision (HDOP)
