Adaptive Extended Kalman Filter Based on Genetic Algorithm for Tightly-Coupled Integrated Inertial and GPS Navigation

Han, Lu; Zhan-rong, Jing; Wei, Mingming; Zhang, Lixin

doi:10.1109/icicta.2009.132

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Therefore, based on the modified version of this estimation proposed by Busse [12] , the formula (8) will be modified to:…”

Section: Online Estimation Of the System Noise Variance Matrix Q Kmentioning

confidence: 99%

Adaptive Extended Kalman Filter in SINS/GPS Tightly Coupled Navigation System

Zhongyuan¹,

Liu²,

Guo³

2015

Advances in Intelligent Systems Research

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Abstract. In the practical application, the SINS/GPS tightly integrated navigation system is faced with the situation that state error model is inaccurate or noise statistical characteristics are inconsistent with the reality. However, traditional Extended Kalman Filter (EKF) method can not effectively solve it, causing large filter errors in this system. This paper presents a method of GDOP estimation and adaptive Extended Kalman Filter, which combines satellite positions of ephemeris with the output of corrected SINS position instead of GPS positioning results to calculate GDOP; On this basis, using the GDOP value and innovation realizes the online real-time estimation of measurement noise variance matrix R k while taking advantage of innovation realizes the online estimation of state noise variance matrix Q k to achieve the adaptive effect and improve navigation accuracy in tightly integrated navigation system.

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“…Therefore, based on the modified version of this estimation proposed by Busse [12] , the formula (8) will be modified to:…”

Section: Online Estimation Of the System Noise Variance Matrix Q Kmentioning

confidence: 99%

Adaptive Extended Kalman Filter in SINS/GPS Tightly Coupled Navigation System

Zhongyuan¹,

Liu²,

Guo³

2015

Advances in Intelligent Systems Research

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“…Ante esta cuestión se han planteado variaciones del algoritmo, como el filtro de Kalman extendido (EKF) (Welch y Bishop, 1995) y el filtro de Kalman unscented (UKF) (Julier y Uhlmann, 2004), que abordan el problema de la no linealidad del sistema y mantienen la estructura de funcionamiento del KF original. Como demuestra la literatura, ambos enfoques -EKF y UKF-han sido aplicados notablemente a la localización de vehículos (Li y Leung, 2003;Zhang et al, 2005;Rezaei y Sengupta, 2007); llegando incluso a emplearse configuraciones de filtros en cascada para reducir la propagación de los errores del modelo (Bevly y Parkinson, 2007), u optimizado a través de su combinación con algoritmos genéticos (Lu et al, 2009). En cuanto al error de los modelos, el algoritmo supone que estos están bajo la influencia de ruidos con distribuciones Gaussianas, lo cual puede llegar a ser un factor limitante para algunas aplicaciones.…”

Section: Trabajos Previosunclassified

Arquitectura de Control para la Conducción Autónoma de Vehículos en Entornos Urbanos y Autovías

Madrid¹

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The parameter optimization of kalman filter based on multi-objective memetic algorithm

Huo

Cai

Gong

et al. 2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Generally, there are two objectives in the optimization of the measurement noise covariance matrix R of Kalman filter. However, most of the traditional optimization methods of Kalman filter only focus on one objective. In this paper, we proposed a new method to optimize the parameter R based on Multi-Objective Memetic Algorithm (MOMA). Compared with traditional methods, it can optimize multiple objectives simultaneously. In this method, the decision vector is the diagonal elements of matrix R, the first objective function f1 is the mean of the residual vectors, and the second objective function f2 is the degree of mismatching between the actual value of the residual covariance with its theoretical value. In the MOMA, the global search based on NSGA-II is utilized to minimize the two objective functions, and the local search based on Simulated Annealing (SA) is just used to minimize the f1. The experimental results demonstrate that the Kalman filter optimized by MOMA, namely MOMA-Kalman, can get much smaller filtering error than regular Kalman filter and other adaptive filter algorithms, such as SageHusa-Kalman and Fuzzy-Kalman.

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Adaptive Extended Kalman Filter Based on Genetic Algorithm for Tightly-Coupled Integrated Inertial and GPS Navigation

Cited by 3 publications

References 4 publications

Adaptive Extended Kalman Filter in SINS/GPS Tightly Coupled Navigation System

Adaptive Extended Kalman Filter in SINS/GPS Tightly Coupled Navigation System

Arquitectura de Control para la Conducción Autónoma de Vehículos en Entornos Urbanos y Autovías

The parameter optimization of kalman filter based on multi-objective memetic algorithm

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