A Modified Nonlinear Two-Filter Smoothing for High-Precision Airborne Integrated GPS and Inertial Navigation

Gong, Xiaolin; Zhang, Jianxu; Fang, Jiancheng

doi:10.1109/tim.2015.2454672

Cited by 42 publications

(14 citation statements)

References 25 publications

(34 reference statements)

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“…The bootstrap particle filter and three different particle smoothers are used to obtain the motion target state estimate. Gong et al [31] have presented a modified nonlinear two-filter smoother for an offline GPS integrated system. The presented smoother has a two-filter design, i.e., forward filter depending on the central difference Kalman filter, and a backward filter is constructed with modified propagation and a smoothing algorithm.…”

Section: Related Workmentioning

confidence: 99%

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

K G*

2019

IJITEE

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The Global Positioning System is extensively used in the various context and location service-based applications. Any kind of abnormalities requires an efficient and suitable pre-processing algorithm to be implemented on the data which provides accurate results when used in the application synchronizations. This paper illustrates a framework for various pre-processing techniques applied to the real-time GPS data and its effect on trajectory mapping. The technique used includes Prioritized pattern-based, Savitzky-Golay filtering, outlier elimination, de-trending, and coefficient correlation. The performance assessment of methods discussed in this study is calculated in terms of accuracy with the original and re-created trajectory after the pre-processing and found that the best result is given by moving window method.

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Section: Related Workmentioning

confidence: 99%

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

K G*

2019

IJITEE

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“…Table 1, the selected model has small mean errors and small SD errors at various heights in comparison with the real PX4FLOW measurements. [42,43]. This estimation error is due to GPS/INS position measurement characteristics, such as the quality of the GPS receiver, multipath errors and the number of satellites in view.…”

Section: Optical Flow Sensor Modelmentioning

confidence: 99%

Precision landing using an adaptive fuzzy multi-sensor data fusion architecture

Al-Sharman

Emran

Jaradat

et al. 2018

Applied Soft Computing

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The positional inaccuracies associated with the GPS/INS measurements make the terminal phase of the normal GPS/INS landing system imprecise. To solve this problem, an adaptive fuzzy data fusion algorithm is developed to obtain more accurate state estimates while the vehicle approaches the landing surface. This algorithm takes the translational displacements in x and y from the mounted Optical Flow (OF) sensor and fuses them with the INS attitude measurements and the altimeter measurements. This low cost adaptive algorithm can be used for vertical landings in areas where GPS outages might happen or in GPS denied areas. The adaptation is governed by imposing appropriate assumptions under which the filter measurement noise matrix R is predicted. The R matrix is continuously adjusted through a fuzzy inference system (FIS) based on the Kalman innovative sequence and the applied covariance-matching technique. This adaptive fuzzy Kalman fusion algorithm (AFKF) is used to estimate the vehicle's states while landing is being commanded. AFKF results are compared with these obtained using a classical Kalman estimation technique. The AFKF algorithm shows better states estimates than its conventional counterpart does. Compared to prior landing systems, the proposed low cost AFKF has achieved a precision landing with less than 10 cm maximum estimated position error. Real precision landing flights were conducted to demonstrate the validity of the proposed intelligent estimation method.

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“…Moreover, a large number of network nodes are required to complete a wide range of indoor target tracking tasks, which is bound to introduce concerns related to network structure optimization as well as multi-node/multi-cluster coordination and communication. Among navigation technologies without beacons, the most commonly used method is the analysis of the pedestrian trajectory by an inertial navigation system [22]. To overcome the shortcomings of a single technology for interior positioning, it is necessary to integrate such technology with multi-sensor information to realize a high-precision, high-reliability, and low-cost positioning system.…”

Section: Introductionmentioning

confidence: 99%

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

Sun

Guan

Chang

et al. 2019

Sensors

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As the restaurant industry is facing labor shortage issues, the use of meal delivery robots instead of waiters/waitresses not only allows the customers to experience the impact of robot technology but also benefits the restaurant business financially by reducing labor costs. Most existing meal delivery robots employ magnetic navigation technologies, which require magnetic strip installation and changes to the restaurant decor. Once the moving path is changed, the magnetic strips need to be re-laid. This study proposes multisource information fusion, i.e., the fusion of ultra-wide band positioning technology with an odometer and a low-cost gyroscope accelerometer, to achieve the positioning of a non-rail meal delivery robot with navigation. By using a low-cost electronic compass and gyroscope accelerometer, the delivery robot can move along a fixed orbit in a flexible and cost-effective manner with steering control. Ultra-wide band (UWB) and track estimation algorithm are combined by extended Kalman filter (EKF), and the positioning error after fusion is about 15 cm, which is accepted by restaurants. In summary, the proposed approach has some potential for commercial applications.

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A Modified Nonlinear Two-Filter Smoothing for High-Precision Airborne Integrated GPS and Inertial Navigation

Cited by 42 publications

References 25 publications

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

PPT-NS: Prioritized Progressive Trend Based Navigational System on GPS data with Artifacts

Precision landing using an adaptive fuzzy multi-sensor data fusion architecture

Design of a Low-Cost Indoor Navigation System for Food Delivery Robot Based on Multi-Sensor Information Fusion

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