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NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models.

Hsu LT, Gu Y, Kamijo S - Sensors (Basel) (2015)

Bottom Line: The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange.Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal.According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

View Article: PubMed Central - PubMed

Affiliation: Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan. qmohsu@kmj.iis.u-tokyo.ac.jp.

ABSTRACT
Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

No MeSH data available.


Positioning Results of the static tests using different methods in the (a) middle and (b) deep urban canyons.
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sensors-15-17329-f017: Positioning Results of the static tests using different methods in the (a) middle and (b) deep urban canyons.

Mentions: This subsection discusses the horizontal positioning error, which contains both the lateral and longitudinal positioning error of the proposed method. We use static data in this discussion because it is difficult to obtain a perfect ground truth of dynamic pedestrian trajectory. Figure 17 shows the static positioning results in both the middle and deep urban canyon environments. In the case of the middle urban canyon, it is obvious that the positioning results of the conventional WLS using only LOS are located on wrong side of the street. Instead, the proposed 3D map method can estimate the result with correct side of the street. To compare the result of the 3D map method before and after the RAIM satellite selection methods, the red points are denser than the yellow points, not only in lateral but also in longitudinal directions. In Table 5, the RAIM satellite selection method slightly improves the 3D map method in the middle urban scenario; however, it improves the 3D map method greatly in the deep urban canyon case. The means of positioning error are about 11.6 and 3.82 m before and after using the RAIM, respectively. Comparing Table 4 and Table 5, the improvements using the RAIM method are about 4.9 m in the lateral error and 7.8 m in horizontal error. Therefore, we can conclude that the proposed RAIM method can reduce not only lateral but also longitudinal positioning error.


NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models.

Hsu LT, Gu Y, Kamijo S - Sensors (Basel) (2015)

Positioning Results of the static tests using different methods in the (a) middle and (b) deep urban canyons.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541937&req=5

sensors-15-17329-f017: Positioning Results of the static tests using different methods in the (a) middle and (b) deep urban canyons.
Mentions: This subsection discusses the horizontal positioning error, which contains both the lateral and longitudinal positioning error of the proposed method. We use static data in this discussion because it is difficult to obtain a perfect ground truth of dynamic pedestrian trajectory. Figure 17 shows the static positioning results in both the middle and deep urban canyon environments. In the case of the middle urban canyon, it is obvious that the positioning results of the conventional WLS using only LOS are located on wrong side of the street. Instead, the proposed 3D map method can estimate the result with correct side of the street. To compare the result of the 3D map method before and after the RAIM satellite selection methods, the red points are denser than the yellow points, not only in lateral but also in longitudinal directions. In Table 5, the RAIM satellite selection method slightly improves the 3D map method in the middle urban scenario; however, it improves the 3D map method greatly in the deep urban canyon case. The means of positioning error are about 11.6 and 3.82 m before and after using the RAIM, respectively. Comparing Table 4 and Table 5, the improvements using the RAIM method are about 4.9 m in the lateral error and 7.8 m in horizontal error. Therefore, we can conclude that the proposed RAIM method can reduce not only lateral but also longitudinal positioning error.

Bottom Line: The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange.Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal.According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

View Article: PubMed Central - PubMed

Affiliation: Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan. qmohsu@kmj.iis.u-tokyo.ac.jp.

ABSTRACT
Currently, global navigation satellite system (GNSS) receivers can provide accurate and reliable positioning service in open-field areas. However, their performance in the downtown areas of cities is still affected by the multipath and none-line-of-sight (NLOS) receptions. This paper proposes a new positioning method using 3D building models and the receiver autonomous integrity monitoring (RAIM) satellite selection method to achieve satisfactory positioning performance in urban area. The 3D building model uses a ray-tracing technique to simulate the line-of-sight (LOS) and NLOS signal travel distance, which is well-known as pseudorange, between the satellite and receiver. The proposed RAIM fault detection and exclusion (FDE) is able to compare the similarity between the raw pseudorange measurement and the simulated pseudorange. The measurement of the satellite will be excluded if the simulated and raw pseudoranges are inconsistent. Because of the assumption of the single reflection in the ray-tracing technique, an inconsistent case indicates it is a double or multiple reflected NLOS signal. According to the experimental results, the RAIM satellite selection technique can reduce by about 8.4% and 36.2% the positioning solutions with large errors (solutions estimated on the wrong side of the road) for the 3D building model method in the middle and deep urban canyon environment, respectively.

No MeSH data available.