A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques

Malleswaran, M.; Vaidehi, V.; Sivasankari, N.

doi:10.1016/j.ast.2013.09.011

Cited by 30 publications

(13 citation statements)

References 8 publications

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“…The major drawback of which is that they cannot store more past vehicle dynamic information when dealing with a time serial data [20]. Therefore, under the condition of a long period of GNSS outages, any of above INS aiding algorithms may not have the capability of providing accurate and stable navigation results [21]. Furthermore, the main shortcoming of the MLP with a sliding window is the rapid increase of computing complexity, as the number of neurons in the input layer has to be equal to the number of past samples [22].…”

Section: Introductionmentioning

confidence: 99%

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

Fang

Jiang

et al. 2020

Remote Sensing

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Aiming to improve the navigation accuracy during global navigation satellite system (GNSS) outages, an algorithm based on long short-term memory (LSTM) is proposed for aiding inertial navigation system (INS). The LSTM algorithm is investigated to generate the pseudo GNSS position increment substituting the GNSS signal. Almost all existing INS aiding algorithms, like the multilayer perceptron neural network (MLP), are based on modeling INS errors and INS outputs ignoring the dependence of the past vehicle dynamic information resulting in poor navigation accuracy. Whereas LSTM is a kind of dynamic neural network constructing a relationship among the present and past information. Therefore, the LSTM algorithm is adopted to attain a more stable and reliable navigation solution during a period of GNSS outages. A set of actual vehicle data was used to verify the navigation accuracy of the proposed algorithm. During 180 s GNSS outages, the test results represent that the LSTM algorithm can enhance the navigation accuracy 95% compared with pure INS algorithm, and 50% of the MLP algorithm.

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Section: Introductionmentioning

confidence: 99%

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

Fang

Jiang

et al. 2020

Remote Sensing

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“…Malleswaran et al presented the Evolutionary Algorithm-based Recurrent Neural Network to improve the position precision of navigation system. 4 The method based on recursive neural network combines genetic algorithm and particle swarm optimization and other evolutionary learning algorithms. The network can operate in training mode and predictive mode, it receives longitude, latitude, and attitude information from INS mechanization.…”

Section: Introductionmentioning

confidence: 99%

Research on a mixed prediction method to vehicle integrated navigation systems

Zhao

Qian

2019

International Journal of Advanced Robotic Systems

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Aiming to improve the positioning accuracy of vehicle integrated navigation system (strapdown inertial navigation system/Global Positioning System) when Global Positioning System signal is blocked, a mixed prediction method combined with radial basis function neural network, time series analysis, and unscented Kalman filter algorithms is proposed. The method is composed by dual modes of radial basis function neural network training and prediction. When Global Positioning System works properly, radial basis function neural network and time series analysis are trained by the error between Global Positioning System and strapdown inertial navigation system. Furthermore, the predicted values of both radial basis function neural network and time series analysis are applied to unscented Kalman filter measurement updates during Global Positioning System outages. The performance of this method is verified by computer simulation. The simulation results indicated that the proposed method can provide higher positioning precision than unscented Kalman filter, especially when Global Positioning System signal temporary outages occur.

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“…The GPS calculates the position and the speed of the aircraft according to the received navigation satellite signals, and has good positioning accuracy, but its anti-disturbance ability is poor. The INS relies entirely on its own sensors to get the position and speed information with strong anti-interference ability, but the navigation error will accumulate over time [1]. So the main idea of GPS/INS integral navigation system is to combine the two systems to make best use of their advantages and bypass their disadvantages, which will improve the overall performance of the navigation system.…”

Section: Introductionmentioning

confidence: 99%

Singular value decomposition aided robust cubature quadrature kalman filter in GPS/INS integrated navigation system

Zhao¹,

Li²,

Zou³

2017

AMA_C

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A novel approach to the integration of GPS and INS using recurrent neural networks with evolutionary optimization techniques

Cited by 30 publications

References 8 publications

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

A LSTM Algorithm Estimating Pseudo Measurements for Aiding INS during GNSS Signal Outages

Research on a mixed prediction method to vehicle integrated navigation systems

Singular value decomposition aided robust cubature quadrature kalman filter in GPS/INS integrated navigation system

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