A New Robust Kalman Filter Algorithm under Outliers and System Uncertainties

Chan, S.C.; Zhang, Z.G.; Tse, K.W.

doi:10.1109/iscas.2005.1465586

Cited by 30 publications

(19 citation statements)

References 8 publications

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“…Since the LS solution suffers from the outlier problem, it can be inferred that the VB-Soft-KF algorithm has the same problem to estimate h r (n). In order to mitigate the effect of outliers caused by unreliable soft symbols, the Huber M estimator can be applied instead of the LS estimation [23]- [25], [31], which finds the solutions according to the following minimization problem…”

Section: Tracking Of Channel Impulse Responsementioning

confidence: 99%

“…(54) can be solved by using the iteratively reweighted least-squares (IRLS) approach [25]. By multiplying and dividing ψ(r i ) in (54) byr i , and defining a diagonal weight matrix Λ with the…”

Section: Tracking Of Channel Impulse Responsementioning

confidence: 99%

“…To solve this problem, we derive an equivalent observation equation for the VBSoft-KF algorithm, which allows reformulating the VB-Soft-KF in a similar way to that for the standard KF. This enables us to extend the robust standard KF [23]- [25] to the VB-Robust-KF under the VB framework. Note that a similar approach of setting equivalent observation equation…”

mentioning

confidence: 99%

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Variational-Bayes-Assisted Joint Signal Detection, Noise Covariance Estimation, and Channel Tracking in MIMO-OFDM Systems

Zhang

Xiao

et al. 2014

IEEE Trans. Veh. Technol.

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This paper introduces an improved variational bayes (Improved-VB) receiver algorithm for joint signal detection, noise covariance matrix estimation and channel impulse response (CIR) tracking in MIMO-OFDM systems over time varying channels. The variational bayes (VB) framework and turbo principle are combined to accomplish the parameter estimation and data detection. In the proposed Improved-VB receiver, a modified linear minimum mean-square-error interference cancellation (LMMSE-IC) soft detector is developed based on the VB theory, which adaptively sets the log-likelihood ratio (LLR) clipping value according to the reliability of detection on each subcarrier to mitigate the error propagation. Following the signal detection, an adaptive noise covariance matrix estimator is derived for the effective noise covariance estimation. Furthermore, in order to track time varying channels, a VB softinput Kalman filter (VB-Soft-KF) is first derived. However, unreliable soft symbols introduce outliers, which degrade the performance of VB-Soft-KF. To tackle this problem, we propose a robust VB softinput Kalman filter (VB-Robust-KF) based on the Huber M estimation theory. Finally, the performance The material has been presented in parts at IEEE PIMRC 2010, Istanbul, Turkey, Sep. 26-30.

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Section: Tracking Of Channel Impulse Responsementioning

confidence: 99%

Section: Tracking Of Channel Impulse Responsementioning

confidence: 99%

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Variational-Bayes-Assisted Joint Signal Detection, Noise Covariance Estimation, and Channel Tracking in MIMO-OFDM Systems

Zhang

Xiao

et al. 2014

IEEE Trans. Veh. Technol.

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“…By applying this sequence of algebraic manipulations in reverse order to (9) and (10), we arrive at the following:…”

Section: Relationship To the Kalman Filtermentioning

confidence: 99%

“…Yet another class of methods uses a weighted least squares approach, as done in robust least squares [8], where each data sample is assigned a weight that indicates its contribution to the hidden state estimate at each time step, e.g., [9]. These methods model the weights as some heuristic function of the data (e.g., the Huber function [8]) and often require manual tuning of threshold parameters for optimal performance.…”

Section: Introductionmentioning

confidence: 99%

Learning an Outlier-Robust Kalman Filter

Ting

Theodorou

Schaal

Machine Learning: ECML 2007

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Abstract. We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quality sensory data (for instance, robotic systems) can be sensitive to data containing outliers. The standard Kalman filter is not robust to outliers, and other variations of the Kalman filter have been proposed to overcome this issue. However, these methods may require manual parameter tuning, use of heuristics or complicated parameter estimation procedures. Our Kalman filter uses a weighted least squares-like approach by introducing weights for each data sample. A data sample with a smaller weight has a weaker contribution when estimating the current time step's state. Using an incremental variational Expectation-Maximization framework, we learn the weights and system dynamics. We evaluate our Kalman filter algorithm on data from a robotic dog.

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Output outlier robust state estimation

Palma

Indiveri

2016

Adaptive Control & Signal

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This work addresses state estimation in presence of outliers in observed data. Outlying data and measurements have a most relevant impact in many control and signal processing applications including marine systems related ones: underwater navigation systems exploiting acoustic data, for example, are frequently affected by outlying measurements. Other on-board sensors and devices are likely to produce measurements contaminated by outlier because of the harsh operating conditions of marine systems. Given the general interest for dealing with measurement outliers in a number of applications, this paper describes a state estimation solution exhibiting robustness to output outliers. The system model is assumed to be linear (either time varying or time invariant) discrete time. The proposed observer is designed by extending an outlier robust static parameter identification algorithm to the case of a linear dynamic plant. The designed estimator has a predictor/corrector structure like the Kalman filter and the Luenberger observer. Simulation and experimental results are provided illustrating the robustness of the derived solution to measurement outliers as compared with the Kalman filter. The proposed solution is also compared with alternative outlier robust state estimation filters showing its effectiveness, in particular, in the presence of measurements outliers occurring in a consecutive sequence. Because of its deterministic execution time and limited numerical complexity, the proposed state estimator can be readily applied in real-time applications. Figure 4. Noisy output with outliers (depicted as blue stars, D 0:2) and non-outliers in grey generated through the model in (41). [Colour figure can be viewed at wileyonlinelibrary.com] The LEL Filter column refers to the interval of singular values over 3000 Monte Carlo runs assuming outliers generated through the model in (41). LEL, least entropy like.with synthetic and real data have been discussed showing the potentiality of the proposed filter as compared with the standard KF. Comparison with alternative robust filtering approaches have shown its robustness to sequences of successive measurement outliers as well. The proposed robust LEL filter may be exploited in real-time applications because of its limited and predictable (i.e. deterministic) computational effort.

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A New Robust Kalman Filter Algorithm under Outliers and System Uncertainties

Cited by 30 publications

References 8 publications

Variational-Bayes-Assisted Joint Signal Detection, Noise Covariance Estimation, and Channel Tracking in MIMO-OFDM Systems

Variational-Bayes-Assisted Joint Signal Detection, Noise Covariance Estimation, and Channel Tracking in MIMO-OFDM Systems

Learning an Outlier-Robust Kalman Filter

Output outlier robust state estimation

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