GPS/INS Enhancement for Land Navigation using Neural Network

Kaygısız, Burak H.; Erkmen, I.; Erkmen, Aydan M.

doi:10.1017/s037346330400267x

Cited by 19 publications

(7 citation statements)

References 3 publications

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“…More specifically, the FFNN architecture is currently being considered as the state-of-the-art method to bridge GPS outages in time-domain solutions (see Chiang et al (2003) ; Kaygisiz et al (2004) ; Jwo and Huang, (2004) ; Chiang et al (2006) ; Wang et al, 2006 ;Kaygisiz et al (2007)). This renders the method sub-optimal, because of the arbitrary character of the activation function.…”

Section: U R R E N T S T a T E -O F -T H E -A R T B R I D G I N G Mmentioning

confidence: 99%

“…For example, one such ANN architecture is the feed-forward neural network (FFNN). More specifically, the FFNN architecture is currently being considered as the state-of-the-art method to bridge GPS outages in time-domain solutions (see Chiang et al (2003); Kaygisiz et al (2004) ; Jwo and Huang, (2004); Chiang et al (2006); Wang et al, 2006; Kaygisiz et al (2007) ). The FFNN is an interconnection of layers (vectors) in which data and calculations flow in a single direction, from the input layer (vector) to hidden layer(s) (vector(s)) and then the output layer (vector).…”

Section: Current State-of-the-art Bridging Models For Gps Outagesmentioning

confidence: 99%

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A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

El-Diasty

Pagiatakis

2010

J. Navigation

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We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution ; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the longterm motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages. K E Y

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Section: U R R E N T S T a T E -O F -T H E -A R T B R I D G I N G Mmentioning

confidence: 99%

Section: Current State-of-the-art Bridging Models For Gps Outagesmentioning

confidence: 99%

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

El-Diasty

Pagiatakis

2010

J. Navigation

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“…Kalman filtering has been applied for many years to provide an optimal GPS/INS integrated module [ 4 , 5 , 6 ]. However, on the one hand, the system performance of a GNSS/INS integrated LVNS entering a tunnel, a downtown area with high buildings, a canyon or a forest may frequently be degrade, bringing gross observation errors [ 4 ] into filtering. INS will drift swiftly out of its planned trajectory over time so that the vehicle may become lost during a mission, especially when a low-cost MEMS inertial measurement unit (IMU) is used.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

Ning

Wang

Han

et al. 2018

Sensors

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Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network’s output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.

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“…A structure of neural network predictions was constructed to offer more precise resolution when a GPS outage is encountered (Kaygisiz et al, 2004). An intelligent position and attitude determination system aided by an artificial neural network (ANN) with conventional Rauch-Tung-Striebel (RTS) smoother was proposed to increase the overall accuracy of a GPS/INS integrated system in post-processing mode by Chiang et al (2009).…”

Section: Introductionmentioning

confidence: 99%

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

Wang

et al. 2014

J. Navigation

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The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages. K E Y

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GPS/INS Enhancement for Land Navigation using Neural Network

Cited by 19 publications

References 3 publications

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

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