Distributed IMM-Unscented Kalman Filter for Speaker Tracking in Microphone Array Networks

Tian, Ye; Chen, Zhe; Yin, Fuliang

doi:10.1109/taslp.2015.2442418

Cited by 35 publications

(13 citation statements)

References 26 publications

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“…However, literature's IMMUFs might fail to estimate a TBM trajectory. In most literature's IMMUFs, the MTM Π is time-invariant [Π(k) = Π(k + 1) for every k ∈ N] [17]- [21]. Nevertheless, with a TBM Π is rarely well approximated by a constant value; for instance, when the TBM system in mode m k = 1, the missile is in the vertical arc phase; and when in mode m k = 2, in the pitch maneuver phase; clearly, Π 1,2 (k) is smaller in the beginning of mode 1 than at its end.…”

Section: B Time-varying Interacting Multiple Model Unscented Filtermentioning

confidence: 99%

“…Nevertheless, IMMUFs set-up remains a difficult subject; it relies on a priori information [15] or dedicated analysis [16]. Besides, most literature's IMMUFs consider the probabilities of the state transitioning between modes constant [17]- [21]. This requires two quite conservative hypotheses: i) that the (non-constant) probability of the TBM transitioning between phases are well approximated by constant values; and ii) that these constant probabilities are a priori known [3], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

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Switching Multiple Model Filter for Boost-Phase Missile Tracking

Menegaz

Battistini

2018

IEEE Trans. Aerosp. Electron. Syst.

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This paper introduces a filter for tracking a ballistic missile during its boost-phase. This filter includes a new switching algorithm and a modified Interacting Multiple Model Unscented Filter (IMMUF) where the Markov Transition Matrix is time-variable. Position, velocity and all unknown parameters of a medium-range ballistic missile model are reconstructed. Simulations demonstrate the new filter is able to consistently estimate a missile's trajectory and all unknown parameters and to outperform previous forms of the IMMUF.

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Section: B Time-varying Interacting Multiple Model Unscented Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Switching Multiple Model Filter for Boost-Phase Missile Tracking

Menegaz

Battistini

2018

IEEE Trans. Aerosp. Electron. Syst.

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“…Compared with the improved algorithm, the EKF is generally seemed as the traditional Kalman filter algorithms, which is firstly proposed by Plett [12]. While these methods have merits of improving the accuracy, the employed filter is based on the premise that the priori statistical properties of noise are known and given [13][14][15][16][17][18][19][20]. However, it is not possible to obtain the accurate statistical characteristics of noise in real applications [21].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

2021

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The traditional Kalman filter algorithms have disadvantages of poor stability (the program cannot converge or crash), robustness (sensitive to the initial errors) and accuracy, partially resulted from the fact that noise covariance matrices in the algorithms need to be set artificially. To overcome the above problems, some adaptive Kalman filter (AKF) algorithms are studied, but the problems still remain unsolved. In this study, two improved AKF algorithms, the improved Sage-Husa and innovation-based adaptive estimation (IAE) algorithms, are proposed. Under the different operating conditions, the estimation accuracy, filter stability, and robustness of the two proposed algorithms are analyzed. Results show that the state of charge (SOC) Max error based on the improved Sage-Husa and the improved IAE is less than 3% and 1.5%, respectively, while the Max errors of the original algorithms is larger than 16% and 4% The two proposed algorithms have higher filter stability than the traditional algorithms. In addition, analyses of the robustness of the two proposed algorithms are carried out by changing the initial parameters, proving that neither are sensitive to the initial errors.

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“…However, the variance of the noise and state can be seen as prior in the KF method. In [24], the time difference of arrival (TDOA) information was calculated by a pair of microphones and a distributed unscented KF method was used to track speakers in a nonlinear measurement model. The method combining KF with compression sensing was also used for dynamic DOA estimation [25,26].…”

Section: Introductionmentioning

confidence: 99%

2-D DOA tracking using variational sparse Bayesian learning embedded with Kalman filter

Huang

et al. 2018

EURASIP J. Adv. Signal Process.

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In this paper, we consider the 2-D direction-of-arrival (DOA) tracking problem. The signals are captured by a uniform spherical array and therefore can be analyzed in the spherical harmonics domain. Exploiting the sparsity of source DOAs in the whole angular region, we propose a novel DOA tracking method to estimate the source locations and trace their trajectories by using the variational sparse Bayesian learning (VSBL) embedded with Kalman filter (KF). First, a transition probabilities (TP) model is used to build the state transition process, which assumes that each source moves to its adjacent grids with equal probability. Second, the states are estimated by KF in the variational E-step of the VSBL and the variances of the state noise and measurement noise are learned in the variational M-step of the VSBL. Finally, the proposed method is extended to deal with the off-grid tracking problem. Simulations show that the proposed method has higher accuracy than VSBL and KF methods.

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Distributed IMM-Unscented Kalman Filter for Speaker Tracking in Microphone Array Networks

Cited by 35 publications

References 26 publications

Switching Multiple Model Filter for Boost-Phase Missile Tracking

Switching Multiple Model Filter for Boost-Phase Missile Tracking

Enhancing Stability and Robustness of State-of-Charge Estimation for Lithium-Ion Batteries by Using Improved Adaptive Kalman Filter Algorithms

2-D DOA tracking using variational sparse Bayesian learning embedded with Kalman filter

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