An EHW architecture for real-time GPS attitude determination based on parallel genetic algorithm

A marine INS/GPS (inertial navigation system/global positioning system) adaptive navigation system is presented in this paper. The GPS with two antennae providing vessel attitude is selected as the auxiliary system to fuse with INS. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The conventional Kalman filter (CKF) assumes that the statistics of the noise of each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However, the GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce a fuzzy logic control method into innovation-based adaptive estimation Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However, how to design the fuzzy logic controller is a very complicated problem, which is still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested in the developed INS/GPS integrated marine navigation system. Real field test results show that the adaptive Kalman filter outperforms the CKF with higher accuracy and robustness. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise existing in the Kalman filter.

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“…The maximum likelihood (ML) concept [9] is used in this subsection to derive the IAE adaptive Kalman filter. Let r = {r 11 , r 22 , .…”

Section: Maximum Likelihood Estimator Of Innovation Adaptive Kalman F...mentioning

confidence: 99%

Study on GPS attitude determination system aided INS using adaptive Kalman filter

Bian

Jin

Tian

2005

Meas. Sci. Technol.

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“…Therefore, optimization and simplification is necessary in such a real-time system. Some improved AFM methods, such as adapting the step of search automatically, genetic algorithm [13,14] and minimizing the number of test points or/and ambiguity candidates [16,17] takes too much time to compute for a real-time GPS attitude determination system; therefore, further research is still necessary.…”

Section: Introductionmentioning

confidence: 99%

Improved ambiguity function method based on analytical resolution for GPS attitude determination

Wang

Zhan

Zhang

2007

Meas. Sci. Technol.

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An improved ambiguity function method (AFM) for GPS attitude determination is presented. The new algorithm is a combination of an analytical resolution and the AFM. The first part of the algorithm is capable of reducing the search integer space by solving a dual nonlinear coupled equation, while the second resolves the adaptive function within the analytical solutions space. Static and kinematic experiments are performed to demonstrate the feasibility of the new algorithm in terms of accuracy, reliability and computation load. Comparisons to traditional algorithm and other measurement tools are given.

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A genetic algorithm for PDA optimal path generation using GPS

Lee

Lee²,

Rittenhouse

2012

Applied Soft Computing

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An EHW architecture for real-time GPS attitude determination based on parallel genetic algorithm

Cited by 14 publications

References 20 publications

Study on GPS attitude determination system aided INS using adaptive Kalman filter

Study on GPS attitude determination system aided INS using adaptive Kalman filter

Improved ambiguity function method based on analytical resolution for GPS attitude determination

A genetic algorithm for PDA optimal path generation using GPS

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