• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.5
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• pp.531-537
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• 2010

Real-time Aerial Monitoring System performs the rapid mapping in an emergency situation so that the geoinformation could be constructed in near real time. In this system, the position and attitude information from GPS/INS integration algorithm is used to perform the aerial triangulation(AT) without GCPs. Therefore, if we obtain Exterior Orientation(EO) estimates from AT sequentially, EO are used as the measurements in the Kalman filter. In this study, we simulate the GPS/IMS/Image data for an UAV-based aerial monitoring system and compare the GPS/INS/AT with and without from AT. Comparative analysis showed that result from the GPS/INS/AT with EO update is more accurate than without the update. However, when the vehicle turns, the position error significantly increases which need more analysis in the future.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.267-275
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• 2013

EKF has been widely used for GPS/INS integration as standard method but EKF has one well-known drawback. if the errors are not within the bounded region, the filter may be divergent. The particle filter has the advantage of the nonlinear and non-gaussian system. This paper proposes a method for compensating the GPS position errors based on the particle filter and presents loosely-coupled GPS/INS integration using proposed algorithm. We used GPS position pattern with particle filter and added attitude kalman filter for improving attitude accuracy. To verify the performance, the proposed method is compared with high cost GPS as reference. In the experimental result, we verified that the accuracy and robust were well improved by the proposed method filter effectively and robustness than by original loosely-coupled integration when vehicle turns at corner.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.151-158
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• 2006

As GPS 'selective availability' was turned off in 2000, GPS related fields and markets are explosively extended. In mapping area, GPS/INS aided photogrammetry proved it is much cost and time effective method keeping enough accuracy as compared with traditional photogrammetry works. The advantage of GPS/INS integration is interdependence. Even if GPS signal was blocked in some time, the position accuracy is not affected. In this study, various GPS signal time gap was used in GPS/INS AT process. Field surveyed ground points were used in accuracy check with GPS/INS AT check points. And the result showed enough accuracy of photogrammetry work rule of NGII. y.

• Korean Journal of Geomatics
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• v.2 no.2
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• pp.99-105
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• 2002

The accuracy of the Digital Photologging System, designed for the construction of the road Facility Database, highly depends on the positions and attitudes of the cameras from GPS/INS integration. In this paper, the development of a loosely coupled GPS/INS is presented. The performance of the system is verified through a simulation as well as a real test data processing. Since the IMU used in this study shows large systematic errors, the possible accuracy of the positions and attitudes of this low-performance IMU when combined with precise GPS positions are assigned. Currently, the integrated system shows the positional accuracy better than 5cm in real data processing. Although the accuracy of attitude based on real test could not be assigned at this time, it is expected that better than 0.5 degrees and 1.8 degrees for horizontal and down component are achievable according to the simulation result.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.984-989
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• 2003

The integrated global position system (GPS) and inertial navigation system (INS) has been considered as a cost-effective way of providing an accurate and reliable navigation system for civil and military system. Even the integration of a navigation sensor as a supporting device requires the development of non-traditional approaches and algorithms. The objective of this paper is to assess the feasibility of integrated with GPS and INS information, to provide the navigation capability for long term accuracy of the integrated system. Advanced algorithms are used to integrate the GPS and INS sensor data. That is fuzzy inference system based Weighted Extended Kalman Filter(FWEKF) algorithm INS signal corrections to provided an accurate navigation system of the integrated GPS and INS. Repeatedly, these include INS error, calculated platform corrections using GPS outputs, velocity corrections, position correction and error model estimation for prediction. Therefore, the paper introduces the newly developed technology which is aimed at achieving high accuracy results with integrated system. Finally, in this paper are given the results of simulation tests of the integrated system and the results show very good performance

• Korean Journal of Geomatics
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• v.3 no.2
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• pp.99-106
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• 2004

In order to meet the users' demand, who needs faster and more accurate data in geographic information, it is necessary to obtain and process the data more effectively. Now more effective data obtainments about geographic information is possible through the development of integration technology, which is applied to the field of geographic information, as well as through the development of hardware and software engineering. With the fast and precise correction and update, the development of integrate technology can bring the reduction of the time and money. To obtain fast and precise geographic information using Aerial Photogrammetry method, it is necessary to develop Airborne GPS/INS integration system, which makes GCP to the minimum. For this reason, this study has tried to develop a system which could unite and process both GPS and INS data. For this matter, code-processing module for DGPS and OTF initializaion module, which can decide integer ambiguity even in motion, have been developed. And also, continuous kinematic carrier-processing module has been developed to calculate the location at the moment of filming. In addition, this study suggests a possibility of using a module, which can unite GPS and INS, using Kalman filtering, and also shows the INS navigation theory.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.127-132
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• 2006

The Global Positioning System (GPS), Inertial Navigation System (INS) and Pseudolite (PL) technologies all play very important roles in navigation systems. As an independent navigation system, GPS can provide high precision positioning results which are independent of time. However, the performance will become unreliable when the system experiences high dynamics, or when the receiver is exposed to jamming or RF interference. In comparison to GPS, though INS is autonomous and provides good short-term accuracy, its use as a standalone navigation system is limited due to the time-dependent growth of the inertial sensor errors. PLs are ground-based transmitters that can transmit GPS-like signals. They have some advantages in that their positions can be determined precisely, and the Signal-to-Noise Ratios (SNR) are relatively high. Because their combined performance, in principle, overcomes the shortcomings of the individual systems, the integration of GPS, INS and PL is increasingly receiving attention from researchers. Depending on the desired performance vs complexity, system integration can be carried out at different levels, namely loose, tight and ultra-tight coupling. Compared with loose and tight integration, although it is more complex in terms of system design, ultra-tight integration will be the basis of the next generation of reliable and robust navigation systems. Its main advantages include improved performance under exposure to high dynamics, and jamming and RF interference mitigation. This paper presents an overview of the ultra-tight integration developments and discusses some of the challenging issues.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.140-145
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• 2013

This paper proposes an efficient integration method for GPS (Global Positioning System) and INS (Inertial Navigation System). To obtain accuracy and computational conveniency at the same time with low cost global positioning system receivers and micro mechanical inertial sensors, a new mechanization method and a new filter architecture are proposed. The proposed mechanization method simplifies velocity and attitude computation by eliminating the need to compute complex transport rate related to the locally-level frame which continuously changes due to unpredictable vehicle motions. The proposed filter architecture adopts two heterogeneous filters, i.e. position-domain Hatch filter and velocity-aided Kalman filter. Due to distict characteristics of the two filters and the distribution of computation into the two hetegrogeneous filters, it eliminates the cascaded filter problem of the conventional loosly-coupled integration method and mitigates the computational burden of the conventional tightly-coupled integration method. An experiment result with field-collected measurements verifies the feasibility of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.780-788
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• 2006

This paper deals with INS/GPS tightly coupled integration algorithms using extend Kalman filter (EKF) and unscented Kalman filter (UKF). In the tightly coupled approach, nonlinear pseudorange measurement models are used for the INS/GPS integration Kalman filter. Usually, an EKF is applied for this task, but it may diverge due to poor functional linearization of the nonlinear measurement. The UKF approximates a distribution about the mean using a set of calculated sigma points and achieves an accurate approximation to at least second-order. We introduce the generalized scaled unscented transformation which modifies the sigma points themselves rather than the nonlinear transformation. The generalized scaled method is used to transform the pseudo range measurement of the tightly coupled approach. To compare the performance of the EKF- and UKF-based tightly coupled approach, real van test and simulation have been carried out with feedforward and feedback indirect Kalman filter forms. The results show that the UKF and EKF have an identical performance in case of the feedback filter form, but the superiority of the UKF is demonstrated in case of the feedforward filer form.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.277-282
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• 2006

It is well known that Kalman filtering is an optimal real-time data fusion method for GPS/INS integration. However, it has some limitations in terms of stability, adaptability and observability. A Kalman filter can perform optimally only when its dynamic model is correctly defined and the noise statistics for the measurement and process are completely known. It is found that estimated Kalman filter states could be influenced by several factors, including vehicle dynamic variations, filter tuning results, and environment changes, etc., which are difficult to model. Neural networks can map input-output relationships without apriori knowledge about them; hence a proper designed neural network is capable of learning and extracting these complex relationships with enough training. This paper presents a GPS/INS integrated system that combines Kalman filtering and neural network algorithms to improve navigation solutions during GPS outages. An Extended Kalman filter estimates INS measurement errors, plus position, velocity and attitude errors etc. Kalman filter states, and gives precise navigation solutions while GPS signals are available. At the same time, a multi-layer neural network is trained to map the vehicle dynamics with corresponding Kalman filter states, at the same rate of measurement update. After the output of the neural network meets a similarity threshold, it can be used to correct INS measurements when no GPS measurements are available. Selecting suitable inputs and outputs of the neural network is critical for this hybrid method. Detailed analysis unveils that some Kalman filter states are highly correlated with vehicle dynamic variations. The filter states that heavily impact system navigation solutions are selected as the neural network outputs. The principle of this hybrid method and the neural network design are presented. Field test data are processed to evaluate the performance of the proposed method.