Observability of Error States in GPS/INS Integration

Hong, Sun In; Lee, Man Hyung; Chun, Ho Hwan; Kwon, Sun-Hong; Speyer, Jason L.

doi:10.1109/tvt.2004.841540

Cited by 178 publications

(112 citation statements)

References 17 publications

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“…The findings here are equivalent to the description in [6], while here this results are related to the velocity measurement scenario, instead of the position, and with a comparison between the 12 and 15 error state models. …”

Section: 12 Error State Modelsupporting

confidence: 70%

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Observability and Performance Analysis of Velocity Measurements with Lever Arm Aided INS

Borko

Klein

Even-Tzur

2017

The 4th International Electronic Conference on Sensors and Applications

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Abstract:In most autonomous vehicles the navigation subsystem is based on Inertial Navigation System (INS). Regardless of the INS grade, its navigation solution drifts in time. To avoid such a drift, the INS is fused with external sensor measurements. Recent publications show that the leverarm, the relative position between the INS and aiding sensor, has influence on the navigation performance. Most published research in this field is focused on INS/GNSS fusion with GNSS position updates only where performance and analytical observability analysis were made to examine the consequence of vehicle maneuvers on the estimation of the lever-arm states. Yet, besides position updates, a variety of sensors measuring the vehicle velocity vector are available including GNSS and a Doppler velocity log. As in position measurements, when performing INS/velocity measurements fusion, the lever-arm must be taken account for. In this paper, performance analysis for velocity measurements with lever-arm aided INS is made for different maneuvers. Two error-states models are used in the analysis. Simulation results show the sensitivity of the error-states to lever arm and vehicle maneuver.

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Section: 12 Error State Modelsupporting

confidence: 70%

“…Publications show that the lever-arm has influence on the navigation performance and observability, of the integrated navigation system [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Observability and Performance Analysis of Velocity Measurements with Lever Arm Aided INS

Borko

Klein

Even-Tzur

2017

The 4th International Electronic Conference on Sensors and Applications

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“…As is known, the error drift of SINS accumulates rapidly with no bound due to the integration of noise-contaminated inertial measurements, so auxiliary information from other sensors is always introduced to mitigate the error drift. Nowadays, global navigation satellite system (GNSS) receivers are the most commonly used sensors because of their high accuracy and low cost [2][3][4]. The performance of SINS/GNSS integrated navigation system relies heavily on the information of GNSS.…”

Section: Introductionmentioning

confidence: 99%

SINS/OD Integrated Navigation Algorithm Based on Body Frame Position Increment for Land Vehicles

Chen¹,

Zhou²,

Liu³

et al. 2018

Mathematical Problems in Engineering

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It is a challenge to achieve high accuracy navigation for land vehicles without the aid of global navigation satellite systems (GNSS). Inertial measurement unit (IMU) and odometer (OD) are widely deployed due to their complementary properties. In this paper, SINS/OD integrated navigation algorithm based on body frame position increment is studied to improve the navigation performance. Taking the calibration errors of odometer scale factor, IMU installation angle, and lever arm into consideration, the odometer measurement model is derived. Then measurement equations based on body frame position increment are proposed to overcome the amplified random errors in the traditional velocity observation approach. Odometer fault detection and exception are conducted based on residual 2 detection method, with the nonholonomic constraints of land vehicles applied to mitigate the standalone SINS error drift. Long distance real test is carried out using laser gyro SINS to assess the proposed algorithm, which shows that navigation performance can be effectively improved.

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“…Furthermore, in many cases, it is quite difficult, even impossible, to measure the distance between the GPS antenna and IMU directly and accurately especially in large vehicles or aircraft. The lever arm error in large vehicles can increase errors in the estimated position, attitude, and inertial sensor biases (Hong et al 2004(Hong et al , 2005(Hong et al , 2006Seo et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Hybrid derivative-free extended Kalman filter for unknown lever arm estimation in tightly coupled DGPS/INS integration

2010

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Differential carrier phase observations from GPS (Global Positioning System) integrated with high-rate sensor measurements, such as those from an inertial navigation system (INS) or an inertial measurement unit (IMU), in a tightly coupled approach can guarantee continuous and precise geo-location information by bridging short outages in GPS and providing a solution even when less than four satellites are visible. However, to be efficient, the integration requires precise knowledge of the lever arm, i.e. the position vector of the GPS antenna relative to the IMU. A previously determined lever arm by direct measurement is not always available in real applications; therefore, an efficient automatic estimation method can be very useful. We propose a new hybrid derivative-free extended Kalman filter for the estimation of the unknown lever arm in tightly coupled GPS/INS integration. The new approach takes advantage of both the linear time propagation of the Kalman filter and the nonlinear measurement propagation of the derivative-free extended Kalman filter. Compared to the unscented Kalman filter, which in recent years is typically used as a superior alternative to the extended Kalman filter for nonlinear estimation, the virtue of the new Kalman filter is equal estimation accuracy at a significantly reduced computational burden. The performance of the new lever arm estimation method is assessed with simulated and real data. Simulations show that the proposed technique can estimate the unknown lever arm correctly provided that maneuvers with attitude changes are performed during initialization. Field test results confirm the effectiveness of the new method.

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Observability of Error States in GPS/INS Integration

Cited by 178 publications

References 17 publications

Observability and Performance Analysis of Velocity Measurements with Lever Arm Aided INS

Observability and Performance Analysis of Velocity Measurements with Lever Arm Aided INS

SINS/OD Integrated Navigation Algorithm Based on Body Frame Position Increment for Land Vehicles

Hybrid derivative-free extended Kalman filter for unknown lever arm estimation in tightly coupled DGPS/INS integration

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