Deeply Coupled GPS/INS integration in pedestrian navigation systems in weak signal conditions

Langer, Markus; Kiesel, Stefan; Ascher, Christian; Trommer, Gert F.

doi:10.1109/ipin.2012.6418913

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…Deeply integrated GNSS/INS systems take into account the advantages of both GNSS and INS, and implement the deepest information fusion between GNSS and INS at present. Compared with loose integration and tight integration, deep GNSS/INS integration not only has better ability of anti-noise and anti-interference [1,2], but also can track weaker satellite signals [3] and acquire the temporarily occluded signals more quickly [4]. All of these advantages are attributed to the use of vector tracking loop [5].…”

Section: Introductionmentioning

confidence: 99%

Fault Identification Ability of a Robust Deeply Integrated GNSS/INS System Assisted by Convolutional Neural Networks

Zou

Lian

Wu³

2019

Sensors

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The problem of fault propagation which exists in the deeply integrated GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) system makes it difficult to identify faults. Once a fault occurs, system performance will be degraded due to the inability to identify and isolate the fault accurately. After analyzing the causes of fault propagation and the difficulty of fault identification, maintaining correct navigation solution is found to be the key to prevent fault propagation from occurring. In order to solve the problem, a novel robust algorithm based on convolutional neural network (CNN) is proposed. The optimal expansion factor of the robust algorithm is obtained adaptively by utilizing CNN, thus the adverse effect of fault on navigation solution can be reduced as much as possible. At last, the fault identification ability is verified by two types of experiments: artificial fault injection and outdoor occlusion. Experiment results show that the proposed robust algorithm which can successfully suppress the fault propagation is an effective solution. The accuracy of fault identification is increased by more than 20% compared with that before improvement, and the robustness of deep GNSS/INS integration is also improved.

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Section: Introductionmentioning

confidence: 99%

Fault Identification Ability of a Robust Deeply Integrated GNSS/INS System Assisted by Convolutional Neural Networks

Zou

Lian

Wu³

2019

Sensors

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“…Although the global position system (GPS) is normally used in pedestrian navigation systems [ 1 , 2 ], alternative techniques are needed for GPS-denied environments such as indoors [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ], urban canyons [ 10 , 11 ], underground [ 12 , 13 , 14 ], and mountain regions where GPS signals are weak or unavailable. Two research methods are mainly used in the indoor position estimation [ 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Navigation Using Foot-Mounted Inertial Sensor and LIDAR

Pham

Suh

2016

Sensors

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Foot-mounted inertial sensors can be used for indoor pedestrian navigation. In this paper, to improve the accuracy of pedestrian location, we propose a method using a distance sensor (LIDAR) in addition to an inertial measurement unit (IMU). The distance sensor is a time of flight range finder with 30 m measurement range (at 33.33 Hz). Using a distance sensor, walls on corridors are automatically detected. The detected walls are used to correct the heading of the pedestrian path. Through experiments, it is shown that the accuracy of the heading is significantly improved using the proposed algorithm. Furthermore, the system is shown to work robustly in indoor environments with many doors and passing people.

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“…Meanwhile, many domestic and foreign scholars have done a lot of research to the acquisition of weak GNSS signal (Fan, 2013;Luo, 2013;Fallahi, 2012;Langer, 2012), among them, the acquisition of weak signal under the influence of strong signal becomes a hotspot and a difficulty nowadays. Due to the attenuation of signal strength in transmission medium, the signal strength which is near the receiver is much higher than the distant signal.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Algorithm for Weak Signal Acquisition under Near-far Effect

2016

Rev. Téc. Ing. Univ. Zulia

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In this correspondence, a novel adaptive algorithm for weak signal acquisition is proposed against the issue of weak signal acquisition under the near-far effect environment. The coarse acquisition of the pseudo-code phase and the carrier frequency are completed first under larger adaptive acquisition step and threshold. On the basis of the results of acquisition, the pseudo-code phase and carrier frequency are refined for the second precise acquisition, which can improve the accuracy of acquisition and Signal to Noise Ratio (SNR) of weak signal. Theoretical analysis and simulation results show that the proposed algorithm is able to weaken the influence of strong signal cross-correlation, increase the detection probability and improve the output SNR of weak signal effectively, applying to both strong and weak signal environment.

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Deeply Coupled GPS/INS integration in pedestrian navigation systems in weak signal conditions

Cited by 9 publications

References 4 publications

Fault Identification Ability of a Robust Deeply Integrated GNSS/INS System Assisted by Convolutional Neural Networks

Fault Identification Ability of a Robust Deeply Integrated GNSS/INS System Assisted by Convolutional Neural Networks

Pedestrian Navigation Using Foot-Mounted Inertial Sensor and LIDAR

An Adaptive Algorithm for Weak Signal Acquisition under Near-far Effect

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