6. An example

mean-shift model for the anomalies. In land applications, these assumptions are likely to hold, though multipath and NLOS may challenge the second one, while the large number of observations available and vulnerabilities increases the computational complexity of FDE proce-

Multipath is the most significant source of measurement errors in ITS applications, as it is dependent on the environment surrounding the antenna and is especially intense in dense urban areas. Buildings and other obstacles degrade the signal reception in three ways: 1) signals are completely blocked and unavailable for positioning, 2) signals are blocked in their direct path, but are still received via a reflected path, with the NLOS reception, 3) both direct Line-Of-Sight (LOS) and reflected signals are received, i.e., the case of multipath. NLOS code signals can exhibit positive ranging errors of tens of meters magnitude in dense urban areas. Numerous innovative techniques have been developed in the recent years to address the multipath and NLOS threats in urban environment. Interest was raised by 3D-map-aided (3DMA) GNSS, a range of different techniques that use 3D mapping data to improve GNSS positioning accuracy in dense urban areas. 3D models of the buildings can be used to predict which signals are blocked and which are directly visible at any location [22, 23]. A technique that determines position by comparing the measured signal availability and strength with predictions made using a 3D city model over a range of candidate positions is known as the shadow matching technique [24]. Such techniques may possibly be integrated with RAIM

The use of precise positioning techniques rather than SPP and the need of integration with other sensors bring a number of complications to the IM for land applications. Some of the

Precise positioning techniques employ carrier phase observations next to code observations. Even though the estimation problem is characterized by a much larger number of observations and unknowns to solve for, it is still a linear estimation problem. The same hypothesis testing theory applies, and therefore, the same RAIM concepts developed for aviation can be implemented, with appropriate adjustments. However, one drawback of the ARAIM is the associated heavy computational burden, due to the need of running a test for each possible combination of simultaneously biased observations. When multi-systems, multi-frequency and carrier phase based positioning is in use, the total number of combinations of possibly biased observations increases dramatically — so does the computational load for the algorithm. It is thus possible

Another issue is constituted by the additional vulnerabilities that affect the precise positioning techniques, mainly carrier phase multipath and cycle-slips. Multipath affects carrier-phase observations with the same mechanism as code observations [25]. Carrier-phase multipath is

dures. These aspects are addressed in more detail in the following.

5.2. Precise positioning techniques and multi-sensor integration

5.1. Urban environment: multipath, NLOS and interference

algorithms for ITS in the near future.

38 Multifunctional Operation and Application of GPS

main challenges are summarized in the following.

5.2.1. PPP and RTK: Carrier phase observations vulnerabilities

that the current ARAIM approach will not be optimal.

In this section, the results of a first attempt to perform IM in urban environment employing the RTK positioning method with a short baseline, and applying a prototype ARAIM algorithm, are shown. Such results are only indicative, since most of the assumptions behind the use of ARAIM in an RTK set-up are yet to be justified.

A kinematic test is conducted for practical demonstration of IM for ITS. A small vehicle is fitted with a Trimble multi-GNSS geodetic receiver and a survey-grade antenna. The test is carried out in a dense urban area in Tokyo, Japan. The RTK system uses GPS, GLONASS and BeiDou dualfrequency observations with a sampling rate of 10 Hz. A prototype RAIM algorithm derived from ARAIM is implemented. Due to the lack of common standards, the PLs are computed in the test using different values of PHMI ranging from 10<sup>3</sup> to 10<sup>6</sup> in order to track empirically the impact of PHMI on the obtained results. A false alert probability (PFA) of 0.01 is applied.

Figure 5 shows the PL for the along-track and cross-track directions (shown as PLAT and PLCT) and the absolute values of the positioning errors along these directions (denoted as errAT and errCT) using an integrity risk of 10�<sup>4</sup> and 10�<sup>6</sup> as examples. The figure shows that the RTK with correct ambiguity fixing gives positioning errors within a few centimeters. The average absolute value of the AT and CT positioning errors are 0.058 and 0.054 m, respectively. The FDE method detected 15 code observations with severe irregularities, which are attributed to high multipath in this environment. These observations were excluded from further processing. There were a few cases where the ambiguity fixing seemed to be incorrect by one or two cycles, which were not detected by the FDE procedure. However, the PL adapted to these situations and bounded this error as illustrated in the Figure 5. Inspection of the Figure also shows that when using RTK with correct ambiguity fixing, an Alert Limit (AL) can be safely chosen as 1 m. The sub-decimeter positioning accuracy of RTK is bounded by a tight protection level. The positioning errors in the test were always bounded by the PLs, and PLs < ALs for the whole period, with an integrity availability of 100%. The medians of the PL for the AT and CT using different integrity risk (PHMI) values are given in the Table 1. Both table and Figure 5 show that the PLs increase with the decrease of the allowed integrity risk.

7. Concluding remarks

Acknowledgements

Author details

Davide Imparato<sup>1</sup>

References

to strengthen the confidence in the models.

edged for providing the RTK kinematic test data in Japan.

, Ahmed El-Mowafy<sup>1</sup>

1 Curtin University, Bentley, WA, Australia

2 UNSW, Kensington, NSW, Australia

ment of Transportation; 2011

tute, ETSI TS 102 637–1 V1.1.1. 2010

\*Address all correspondence to: a.el-mowafy@curtin.edu.au

In this chapter the concepts of integrity and IM have been introduced, and the main RAIM methods currently in use or under development have been presented. As these methods were developed in the aviation context, their adoption in land applications has been discussed. The positioning methods used in land applications still satisfy the assumptions made by current RAIM algorithms, though great care shall be taken in addressing the larger number of vulnerabilities affecting the positioning system, in particular multipath and the carrier-phase specific vulnerabilities. Some preliminary but promising results of the application of a RAIM algorithm in urban environment were shown. Further research and practical experiments are necessary

Integrity Monitoring: From Airborne to Land Applications

http://dx.doi.org/10.5772/intechopen.75777

41

Dr. Nobuaki Kubo from Tokyo University of Marine Sciences and Technology is acknowl-

\* and Chris Rizos2

[1] Research and Innovative Technology Administration. Connected Vehicle Technology: Safety Pilot Driver Acceptance Clinic Overview. RITA. Washington, DC, US: US Depart-

[2] ETSI. Intelligent Transport Systems (ITS). Vehicular communications; basic set of applications; part 1: Functional requirements. In: European Telecommunications Standards Insti-

[3] Enge P, Walter T, Pullen S, Kee C, Chao YC, Tsai YJ. Wide area augmentation of the global

[4] Roturier B, Chatre E, Ventura-Traveset J. The SBAS integrity concept standardised by

positioning system. Proceedings of the IEEE. 1996;84(8):1063-1088

ICAO. Application to EGNOS. Navigation-Paris. 2001;49:65-77

Figure 5. PLAT and PLCT and positioning errors in the AT and CT directions for the integrated positioning systems using <sup>P</sup>HMI <sup>¼</sup> <sup>10</sup>�<sup>4</sup> (top panel) and <sup>P</sup>HMI <sup>¼</sup> <sup>10</sup>�<sup>6</sup> (bottom panel).


Table 1. Median PLAT & PLCT in meters for different values of integrity risk (PHMI).
