Robust Adaptive Kalman Filtering – a method based on quasi-accurate detection and plant noise variance–covariance matrix tuning

Luo, Xiaowen; Wang, Haitao

doi:10.1017/s0373463316000564

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…It is known that the noise covariance is related to the environment but the parameters are preset empirically [25] [33]. In the future work, a mathematical model of cognitive radar along with a new cost function is deserved to be built jointly by combining more variables, wherein the tuning parameters in the noise covariance update equation can be adaptive to the changing environment in real-time.…”

Section: Discussionmentioning

confidence: 99%

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Zhong

Cheng

et al. 2020

IEEE Access

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This paper presents an approach for cognitive radar to track the target state despite unknown statistics or sudden changes in noise exposed to uncertain environments. Given the prior knowledge or accurate estimation of the noise, cognitive radar can adaptively adjust the waveform in the transmitter by perceiving the environment. However, the measurement noise usually contains a priori unknown parameters which are difficult to be estimated, or the process noise without empirical model might be improperly configured. Besides, they may change abruptly or time-varying in practice. These issues are prone to occur in cognitive radar, leading to performance decline or failure in tracking ultimately. To alleviate this problem, a robust cognitive radar tracking method based on adaptive unscented Kalman filter (AUKF) is proposed in this paper. Specifically, UKF is used to derive the cognitive mathematical model and estimate the states in nonlinear systems. Moreover, a robust adaptive mechanism is designed in the cognitive framework, which is independent of prior information. When the mismatch between the noise in the model and the noise in the environment emerges, the noise covariance can be corrected adaptively. To verify the efficiency of the scheme, maneuvering target tracking experiments are carried out in three uncertain noise scenarios. Simulation results show that the scheme outperforms the existing adaptive UKF and cognitive radar algorithms in terms of intelligence and robustness. INDEX TERMS cognitive radar, adaptive filtering, robust estimation, target tracking, uncertain noise.

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Section: Discussionmentioning

confidence: 99%

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Zhong

Cheng

et al. 2020

IEEE Access

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“…In Equation (15), the dynamic lever-arm is undetermined in acceleration matching-based TA, and is usually treated as a measurement error. When processing TA with KF, the algorithm is converges well only when the measurement error is a white noise [ 27 , 28 ]. In other words, when the measurement error is colored noise, an estimation error will occur in KF.…”

Section: Methodsmentioning

confidence: 99%

A New Bias Error Prediction Model for High-Precision Transfer Alignment

Zhang

Yang

Qin

et al. 2018

Sensors

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The purpose of this work was to study bias error in acceleration-based transfer alignment, which is probably caused by cross-correlation between the dynamic lever-arm and the linear motion of a ship. A new prediction model for the cross-correlation-caused error is proposed in this paper. We adopt the Bernoulli-Euler Beam as a simplified ship hull-girder model to verify the existence of the cross-correlation. Then, the mathematical mechanism and the prediction model of the bias error are deduced via the ordinary least squares theory. Three factors influence the bias error in the prediction model: the amplitude of the dynamic lever arm acceleration, the amplitude of the ship motion acceleration, and the cross-correlation between them. Simulation experiments are then conducted to test the influence of the factors. The results show that the mechanism analysis is reasonable, and the bias error prediction model is in agreement with the experimental results. Thus, the proposed prediction model has the potential to deduce the bias error in high-accuracy transfer alignment.

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“…A loosely coupled GPS/INS integration that tuned its R of the Kalman filter by the innovation/residual between the measurement and propagation is proposed [31]. To improve the residual-based adaptive Kalman filter (AKF), a quasi-accurate detection method is proposed to solve the noise generated by abrupt motion changes [32]. Hajiyev and Soken developed a robust AKF to isolate sensor/actuator faults by assigning multiple adaptive factors for both Q and R [33].…”

Section: Introductionmentioning

confidence: 99%

Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system

Zhang

Hsu

2018

Aerospace Science and Technology

101

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Owing to the increase in civil applications using quadcopters, commercial flight control systems such as Pixhawk are a popular solution to provide the sensing and control functions of an unmanned aerial vehicle (UAV). A low-cost global navigation satellite system (GNSS) receiver is crucial for the low-cost flight control system. However, the accuracy of GNSS positioning is severely degraded by the notorious multipath effect in mega-urbanized cities. The multipath effect cannot be eliminated but can be mitigated; hence, the GNSS/inertial navigation system (INS) integrated navigation is a popular approach to reduce this error. This study proposes an adaptive Kalman filter for adjusting the noise covariance of GNSS measurements under different positioning accuracies. The adaptive tuning is based on a proposed accuracy classification model trained by a supervised machine-learning method. First, principle component analysis is employed to identify the significant GNSS accuracy related features. Subsequently, the positioning accuracy model is trained based on a random forest learning algorithm with the labeled real GNSS dataset encompassing most scenarios concerning modern urban areas. To reduce the cases of misclassifying the GNSS accuracy, a fuzzy logic algorithm is employed to consider the GNSS accuracy propagation. Additionally, the process noise covariance of the INS is determined using the Allan variance analysis. The positioning performance of the proposed adaptive Kalman filter is compared with both a conventional Kalman filter and the positioning solution provided by the commercial flight control system, Pixhawk 2. The results show that the proposed adaptive Kalman filter using random forest with fuzzy logic can achieve a better classification of GNSS accuracy compared to the others. The overall positioning result improved by approximately 50% compared with the onboard solution.

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Robust Adaptive Kalman Filtering – a method based on quasi-accurate detection and plant noise variance–covariance matrix tuning

Cited by 9 publications

References 13 publications

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

Robust Cognitive Radar Tracking Based on Adaptive Unscented Kalman Filter in Uncertain Environments

A New Bias Error Prediction Model for High-Precision Transfer Alignment

Intelligent GNSS/INS integrated navigation system for a commercial UAV flight control system

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