Development of GPS/INS integration module using Kalman filter

Yadav, Nagesh; Shanmukha, Abhishek; Amruth, B. M.; Basavaraj,

doi:10.1109/icammaet.2017.8186715

Cited by 8 publications

(3 citation statements)

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“…In order to eliminate these unknown quantities, the pseudo-range equation and the Doppler shift equation obtained by different satellites are subtracted by using the principle of single-difference. 15 These equations are as follows:…”

Section: Establishment Of Measurement Equation For Gps Subsystemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The commonly used location fusion algorithm is realized by means of EKF, UKF, and CKF. [14][15][16] However, in the fusion algorithm of GPS and WLAN, WLAN cannot provide speed information, so it is impossible for users in motion to accurately construct the state equation needed in Kalman filter algorithm.This will greatly affect the final positioning results. Xia et al 17 proposed to obtain the initial plane position by means of WiFi positioning, and then use the a priori elevation information and three GPS satellite observations to achieve positioning.…”

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confidence: 99%

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Integrated positioning algorithm based on GPS/WLAN

Zheng

Zhao

et al. 2019

Concurrency and Computation

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In complex urban environment, GPS signals are susceptible to occlusion and interference; the positioning accuracy will be reduced or even impossible to locate. Today, Wireless Local Area Network (WLAN) localization technology can effectively supplement the blind spot of GPS signals. However, due to the instability of received signal strength (RSS), the positioning accuracy of WLAN is seriously affected. To solve the aforementioned problems, a GPS/WLAN integrated location algorithm based on particle filter is proposed. In our method, the federated Kalman filter is used to fuse the location information of GPS and WLAN, and then the important sampling function of the particle filter is generated by the fusion result. The updating of the particle weights is accomplished by the results of federated Kalman filter and map information. With the complementary characteristics of GPS and WLAN positioning technology, the experimental results show that our algorithm can effectively improves the coverage rate of GPS and the positioning accuracy of pedestrians in complex urban environment. Compared with GPS and WLAN single system and traditional fusion algorithm, the positioning accuracy of the proposed algorithm is significantly improved. KEYWORDSFederated Kalman filter, GPS/WLAN, information fusion, particle filter INTRODUCTIONGPS is an all-weather, full time satellite navigation system that developed by the US Department of defense. With the good positioning performance, GPS is the essential around the world. However, It shows that when users are in hot spots such as urban canyons, buildings, and other activities, terrible positioning accuracy will occur. 1 It means that the navigation technology are facing the following challenges: (1) GPS signal is susceptible to interference.(2) Its number of visible satellites is small.With the rapid development of wireless network technology and pervasive computing, Location-Based Services (LBS) is attracting more and more attention from industry and academia. Obviously, positioning technology is the key to LBS. Therefore, with the boom of LBS and the urgent need for location services in urban environments, wireless location technology has developed rapidly. It includes WLAN, RFID, UWB, Zigbee, Bluetooth, Infrared, and other positioning methods. 2 However, a single signal often cannot cover all the space in an urban environment, so wireless positioning technology is required to be combined with GPS.In order to further improve the positioning accuracy of the combined system while expanding the coverage of the positioning system, and considering the extensive laying of WLAN, this paper proposes a GPS/WLAN combinatorial positioning algorithm based on improved particle filter algorithm. In the particle filtering process, which is fused with GPS and WLAN, the importance sampling function is generated by the federated Kalman filter. The weight of the particle is determined by the building map information and the distance between each particle and the positioning result. The simulation and exp...

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Section: Establishment Of Measurement Equation For Gps Subsystemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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