Huber’s M-Estimation-Based Process Uncertainty Robust Filter for Integrated INS/GPS

Chang, Lubin; Li, Kailong; Hu, Baiqing

doi:10.1109/jsen.2014.2384492

Cited by 111 publications

(62 citation statements)

References 27 publications

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“…Because of its clearness and convenience in computer calculation, the Kalman filter has been the classical method in the filtering and estimation of Gaussian stochastic systems [28,29]. It is applied widely in target tracking [30], integrated navigation [31], communication signal processing [32], etc. Kalman filter introduces the state space description in the time domain, in which the estimated signal is set as the output of the stochastic linear system in the action of white noise.…”

Section: Kalman Filter and Its Improvementmentioning

confidence: 99%

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai

Wang

Jin

et al. 2020

Sensors

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The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

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Section: Kalman Filter and Its Improvementmentioning

confidence: 99%

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai

Wang

Jin

et al. 2020

Sensors

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“…Because of vehicle’s maneuvering and external airflow impact, the condition of optimal estimation is difficult to ensure in practical applications, resulting in filtering accuracy decreasing or even filtering divergence. From the perspective of the approximate Bayesian estimation, the essence of the Huber filter is adding a weight matrix before the innovation, to truncate the average of filter innovations, thereby suppressing the effect of interference noise or outliers in system observation information and enhancing its robustness [ 22 , 31 , 32 ]. Assumed in a nonlinear system, the transformation innovation probability density based on the Huber cost function can be used to calculate the transformation innovation

wherein

is the autocorrelation covariance matrix of the observation,

and

are the actual observation and the predicted observation.…”

Section: Initial Alignment Algorithm For the Unmanned Vehiclementioning

confidence: 99%

An Improved Strapdown Inertial Navigation System Initial Alignment Algorithm for Unmanned Vehicles

Zhang

Gao

et al. 2018

Sensors

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Along with the development of computer technology and informatization, the unmanned vehicle has become an important equipment in military, civil and some other fields. The navigation system is the basis and core of realizing the autonomous control and completing the task for unmanned vehicles, and the Strapdown Inertial Navigation System (SINS) is the preferred due to its autonomy and independence. The initial alignment technique is the premise and the foundation of the SINS, whose performance is susceptible to system nonlinearity and uncertainty. To improving system performance for SINS, an improved initial alignment algorithm is proposed in this manuscript. In the procedure of this presented initial alignment algorithm, the original signal of inertial sensors is denoised by utilizing the improved signal denoising method based on the Empirical Mode Decomposition (EMD) and the Extreme Learning Machine (ELM) firstly to suppress the high-frequency noise on coarse alignment. Afterwards, the accuracy and reliability of initial alignment is further enhanced by utilizing an improved Robust Huber Cubarure Kalman Filer (RHCKF) method to minimize the influence of system nonlinearity and uncertainty on the fine alignment. In addition, real tests are used to verify the availability and superiority of this proposed initial alignment algorithm.

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“…7). Filter parameter N used in (12) and (14) was set to N = 5 which corresponds to the cut-off frequency fcut ≈ 18 Hz at the sampling rate 512 Hz. The absolute errors of the constant gain and the adaptive gain fusion are compared in Fig.…”

Section: B Sensor Fusion Experimentsmentioning

confidence: 99%

“…The secondary sensor may be much noisier and have slower response but its error has to be kept inside fixed bounds. A modification of the Kalman filter can be used as a [12]. We have proposed a heterogeneous sensor fusion method for one differential sensor and one absolute sensor which requires only minimum count of parameters independently from the measured system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Real-Time MEMS Sensor Fusion and Calibration

et al. 2016

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This paper discusses an innovative adaptive heterogeneous fusion algorithm based on estimation of the mean square error of all variables used in real time processing. The algorithm is designed for a fusion between derivative and absolute sensors and is explained by the fusion of the 3-axial gyroscope, 3-axial accelerometer and 3-axial magnetometer into attitude and heading estimation. Our algorithm has similar error performance in the steady state but much faster dynamic response compared to the fixed-gain fusion algorithm. In comparison with the extended Kalman filter the proposed algorithm converges faster and takes less computational time. On the other hand, Kalman filter has smaller mean square output error in a steady state but becomes unstable if the estimated state changes too rapidly. Additionally, the noisy fusion deviation can be used in the process of calibration. The paper proposes and explains a real-time calibration method based on machine learning working in the online mode during run-time. This allows compensation of sensor thermal drift right in the sensor's working environment without need of re-calibration in the laboratory.

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Huber’s M-Estimation-Based Process Uncertainty Robust Filter for Integrated INS/GPS

Cited by 111 publications

References 27 publications

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

An Improved Strapdown Inertial Navigation System Initial Alignment Algorithm for Unmanned Vehicles

Intelligent Real-Time MEMS Sensor Fusion and Calibration

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