Observability Analysis of Non-Holonomic Constraints for Land-Vehicle Navigation Systems

Niu, Xiaoji; Li, You; Zhang, Quan; Cheng, Yahao; Shi, Chuang

doi:10.5081/jgps.11.1.80

Cited by 36 publications

(31 citation statements)

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“…In [38,39], the piecewise constant system (PWCS) method is used to analyse the observability of a time variant system. The global observability is applied to analyse navigation states' observability of integrated navigation systems [40] and non-holonomic constraints [41,42]. According to [42,43] and [44], under different maneuvering, unobservable states will become observable.…”

Section: Observability Analysismentioning

confidence: 99%

“…The global observability is applied to analyse navigation states' observability of integrated navigation systems [40] and non-holonomic constraints [41,42]. According to [42,43] and [44], under different maneuvering, unobservable states will become observable. The researchers introduce the concept of degree of observability, which reflects the effect of maneuvering to state estimation in [45][46][47].…”

Section: Observability Analysismentioning

confidence: 99%

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Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

Zhao

2016

Signal Processing

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Section: Observability Analysismentioning

confidence: 99%

Section: Observability Analysismentioning

confidence: 99%

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

Zhao

2016

Signal Processing

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“…However, GNSS faces problems such as signal blockage and multipath in urban areas and other GNSS-challenging environments. In these cases, GNSS/INS (inertial navigation system) integrated systems are usually used for navigation applications [3][4][5]. However, GNSS/INS integrated systems cannot work well for pedestrian navigation in deep indoor environments where GNSS is totally unavailable.…”

Section: Introductionmentioning

confidence: 99%

PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

et al. 2015

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Abstract:Providing an accurate and practical navigation solution anywhere with portable devices, such as smartphones, is still a challenge, especially in environments where global navigation satellite systems (GNSS) signals are not available or are degraded. This paper proposes a new algorithm that integrates inertial navigation system (INS) and pedestrian dead reckoning (PDR) to combine the advantages of both mechanizations for micro-electro-mechanical systems (MEMS) sensors in pedestrian navigation applications. In this PDR/INS integration algorithm, a pseudo-velocity-vector, which is composed of the PDR-derived forward speed and zero lateral and vertical speeds from non-holonomic constraints (NHC), works as an update for the INS to limit the velocity errors. To further limit the drift of MEMS inertial sensors, trilateration-based WiFi positions with small variances are also selected as updates for the PDR/INS integrated system. The experiments illustrate that positioning error is decreased by 60%-75% by using the proposed PDR/INS integrated MEMS solution when compared with PDR. The positioning error is further decreased by 15%-55% if the proposed PDR/INS/WiFi integrated solution is implemented. The average accuracy of the proposed PDR/INS/WiFi integration algorithm achieves 4.5 m in indoor environments.

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“…Calibration is a useful way to remove many deterministic sensor errors and improve sensor-based navigation [10]; however, MEMS inertial sensors suffer from significant run-to-run biases and thermal drifts [11]. Especially, the heading error will grow when there is no aiding information [12]. Magnetometers can assist the heading estimation by sensing the geomagnetic field [13].…”

Section: Introductionmentioning

confidence: 99%

WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices

Zhuang

Lan

et al. 2015

Micromachines

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This paper presents a WiFi-aided magnetic matching (MM) algorithm for indoor pedestrian navigation with consumer portable devices. This algorithm reduces both the mismatching rate (i.e., the rate of matching to an incorrect point that is more than 20 m away from the true value) and computational load of MM by using WiFi positioning solutions to limit the MM search space. Walking tests with Samsung Galaxy S3 and S4 smartphones in two different indoor environments (i.e., Environment #1 with abundant WiFi APs and significant magnetic features, and Environment #2 with less WiFi and magnetic information) were conducted to evaluate the proposed algorithm. It was found that WiFi fingerprinting accuracy is related to the signal distributions. MM provided results with small fluctuations but had a significant mismatch rate; when aided by WiFi, MM's robustness was significantly improved. The outcome of this research indicates that WiFi and MM have complementary characteristics as the former is a point-by-point matching approach and the latter is based on profile-matching. Furthermore, performance improvement through integrating WiFi and MM depends on the environment (e.g., the signal distributions of magnetic intensity and WiFi RSS): In Environment #1 tests, WiFi-aided MM and WiFi provided similar results; in Environment #2 tests, the former was approximately 41.6% better. Our results supported that the WiFi-aided MM algorithm provided more reliable solutions than both WiFi and MM in the areas that have poor WiFi signal distribution or indistinctive magnetic-gradient features. OPEN ACCESSMicromachines 2015, 6 748

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Observability Analysis of Non-Holonomic Constraints for Land-Vehicle Navigation Systems

Cited by 36 publications

References 0 publications

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

Performance evaluation of Cubature Kalman filter in a GPS/IMU tightly-coupled navigation system

PDR/INS/WiFi Integration Based on Handheld Devices for Indoor Pedestrian Navigation

WiFi-Aided Magnetic Matching for Indoor Navigation with Consumer Portable Devices

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