Practical approach in estimating inertial navigation unit's errors

Lim, Chot Hun; Tan, Wei; Lim, Theam Soon; Koo, Voon Chet

doi:10.1587/elex.9.772

Cited by 10 publications

(15 citation statements)

References 4 publications

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“…High-performance inertial sensors are commonly being controlled by government regulations, resulting in unattainable of the sensors in civilian applications. On the contrary, the low-cost, low-performance SINU sensors can be easily acquired, but its measurement data suffered from various errors [23] that jeopardized its accuracy. Due to this issue, the GPS data are adopted as an external reference source to minimize the SINU's errors through the implementation of the Kalman filter.…”

Section: Real-time Gps-aided Inu Systemmentioning

confidence: 99%

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Kalman Filtering and Its Real‐Time Applications

Hun¹,

Yeng²,

Sze³

et al. 2016

Real-Time Systems

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Kalman filter was pioneered by Rudolf Emil Kalman in 1960, originally designed and developed to solve the navigation problem in Apollo Project. Since then, numerous applications were developed with the implementation of Kalman filter, such as applications in the fields of navigation and computer vision's object tracking. Kalman filter consists of two separate processes, namely the prediction process and the measurement process, which work in a recursive manner. Both processes are modeled by groups of equations in the state space model to achieve optimal estimation outputs. Prior knowledge on the state space model is needed, and it differs between different systems. In this chapter, the authors outlined and explained the fundamental Kalman filtering model in real-time discrete form and devised two real-time applications that implemented Kalman filter. The first application involved using vision camera to perform realtime image processing for vehicle tracking, whereas the second application discussed the real-time Global Positioning System (GPS)-aided Strapdown Inertial Navigation Unit (SINU) system implementation using Kalman filter. Detail descriptions, model derivations, and results are outlined in both applications.

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Section: Real-time Gps-aided Inu Systemmentioning

confidence: 99%

“…Real-time Systems respect to i-frame projected in b-frame. Note that both δs b and δω ib b are the random errors that reside in the SINU that causes the inaccuracy of the sensor [23].…”

Section: Dynamic Error Model Of Inertial Navigation Equationsmentioning

confidence: 99%

Kalman Filtering and Its Real‐Time Applications

Hun¹,

Yeng²,

Sze³

et al. 2016

Real-Time Systems

View full text Add to dashboard Cite

show abstract

“…Where: ω (rad/s) ∆t = The discrete sampling time (s) Equation1 measurements are corrupted by deterministic errors and stochastic errors (Lim et al, 2012a). Note that the deterministic errors can be removed through proper calibrations.…”

Section: Gps-aided Insmentioning

confidence: 99%

“…With recent advancement in Micro-ElectroMechanical System (MEMS), the INU is able to be built on top of a small size, low cost Integrated Circuit (IC) chip (Nebot and Durrant, 1999). However, such INU configuration still fail to work as a standalone device for navigation applications due to its resided stochastic noise (El-Diasty and Spiros, 2008;Sheimy et al, 2008;Lim et al, 2012a). A conventional approach to solve this issue is to fuse the Global Positioning System (GPS) data with the INU measurement using Kalman filter, where such configuration is commonly known as GPS-aided INS (Alison, 2005;David et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Implementation of Anfis for GPS-Aided Ins Uav Motion Sensing at Short Term GPS Outage

Lim¹,

Lim²,

Koo³

2014

Journal of Computer Science

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The recent improvement in Micro-Electro-Mechanical System (MEMS) technology has enabled the evolvement of Inertial Navigation Unit (INU) to be built on top of a low cost, small size Integrated Circuit (IC) chip. Due to the nature of the MEMS INU, its outputs are normally corrupted by the resided stochastic noise. A common practice to regulate its measurements into usable motion data is by fusing the Global Positioning System (GPS) measurement data with the MEMS INU measurement data through Kalman filter for position, velocity and orientation estimations. Such integrated system is known as GPS-aided Inertial Navigation System (INS). Note that the robustness of the GPS-aided INS relies heavily on the availability of the GPS signals. In the event of no GPS signals, the overall system will solely depend on the INU to predict the position, velocity and orientation. The prediction results will eventually drift from its true value due to the INU's resided stochastic noise. In this study, a remedy system using Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to improve the performance of the GPS-aided INS during GPS outage condition. UAV motion sensing experiment was carried out and GPS outage conditions were imposed at several locations during the UAV navigation. The motion prediction dataduring GPS outages, with and without ANFIS implementation, were compared and the results clearly show that the GPS-aided INS with ANFIS implementation achieved better performance than the GPS-aided INS without ANFIS.

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“…The update rates of GPS and SINU were 5Hz and 40Hz, respectively. Calibration [4] was performed on the SINU before carrying out the experiments. Figure 2 and Figure 3 show the offline comparison of the accelerometersʹ and gyroscopesʹ error estimations in one of the field experiments.…”

Section: Kalman Filter Modellingmentioning

confidence: 99%

Design and Development of a Real-Time GPS-Aided SINU System

Lim

Koo

2012

International Journal of Advanced Robotic Systems

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A Strapdown Inertial Navigation Unit (SINU) is a low cost motion measurement device commonly used for navigation solutions. Global Positioning System (GPS) is usually adopted as an external reference source to minimize the SINUʹs accumulation errors by applying a Kalman filter to obtain best estimations in positions, velocities and orientations. However, due to the low sampling rate of GPS, such a configuration does not provide intensive orientation estimation. In this paper, a new and efficient real-time GPSaided SINU system with incorporated magnetometers is developed to enhance the orientation estimation. An intensive orientation estimation algorithm is developed by combining the extra sensory inputs from magnetometers with the inputs from accelerometers. The estimated orientation was applied in the Kalman filtering, replacing the GPS-aided orientation estimation. The offline implementation shows promising results in reducing both the accelerometersʹ and gyroscopesʹ errors. Finally, the design is successfully implemented in real-time.

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Practical approach in estimating inertial navigation unit's errors

Cited by 10 publications

References 4 publications

Kalman Filtering and Its Real‐Time Applications

Kalman Filtering and Its Real‐Time Applications

Implementation of Anfis for GPS-Aided Ins Uav Motion Sensing at Short Term GPS Outage

Design and Development of a Real-Time GPS-Aided SINU System

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