Effective Improvement of Under-Modeling Frequency-Domain Kalman Filter

Fan, Wenzhi; Chen, Kai; Lü, Jing; Tao, Jianmin

doi:10.1109/lsp.2019.2890965

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…Originally, FDKF is widely used in AEC problem [36] [37]. In this part, we adapt the fuel cell prognosis problem into the form that can be solved by FDKF, which is shown as figure 4.…”

Section: Frequency Domain Kalman Filtermentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Prognosis Based on Frequency-Domain Kalman Filter

Laghrouche²,

Depernet³

et al. 2021

IEEE Trans. Transp. Electrific.

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The degradation seriously affects the durability and cost of the Proton Exchange Membrane Fuel Cell (PEMFC). This paper presents a novel model-driven method based on the Frequency Domain Kalman Filter (FDKF) and voltage degradation model to predict the degradation of PEMFC in the frequency domain.The advantage of the proposed FDKF method is that it can process the data in groups; thus the computation time can be greatly reduced with high accuracy. Two degradation experiments under constant and quasi-dynamic currents have been used to demonstrate its prognosis performances under different conditions and different training times. Compared with the traditional time domain Extended Kalman Filter method and literature, it has been demonstrated that the proposed one has higher accuracy and requires much less calculation time.

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“…Originally, FDKF is widely used in AEC problem [36] [37]. In this part, we adapt the fuel cell prognosis problem into the form that can be solved by FDKF, which is shown as figure 4.…”

Section: Frequency Domain Kalman Filtermentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Prognosis Based on Frequency-Domain Kalman Filter

Laghrouche²,

Depernet³

et al. 2021

IEEE Trans. Transp. Electrific.

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“…The value of a is set to 2 instead of 0.5 when b(n) ≤ 0 for smoother non-linearity. For the convolution operation, we construct 50 simulated rooms, each dimension of which is randomly chosen from [2,5] m and T 60 is randomly chosen from [150, 450] ms. A microphone and a loudspeaker are randomly placed in each room for generation of room impulse responses (RIRs) by the image method [21]. Four hundred of these RIRs are used to generate the training set while the rest one hundred RIRs are used to generate the test set.…”

Section: Datasetmentioning

confidence: 99%

“…Frequencydomain least mean square algorithms are often utilized to guarantee both fast convergence speed and low computational load [2]. The frequency-domain adaptive Kalman filter (FDKF) [3] is also a commonly used method with several efficient variations proposed recently [4,5].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet

Chen¹,

Xiang²,

Chen³

et al. 2020

Preprint

Self Cite

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Acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and farend signal. Usually a post processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of fully convolutional time-domain audio separation network (Conv-TasNet). Both the residual signal of the linear acoustic echo cancellation system, and the output of the adaptive filter are adopted to form multiple streams for the Conv-TasNet, resulting in more effective echo suppression while keeping a lower latency of the whole system. Simulation results validate the efficacy of the proposed method in both single-talk and double-talk situations.

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“…It is found that the PFKF converges to a biased steady-state solution when the filter is of deficient length and the performance might deteriorate considerably. The normal frequency-domain Kalman filter and other frequency-domain adaptive filters also suffer from similar problems [20]- [23]. To resolve the problem of performance deterioration, a modification of the PFKF is proposed on the basis of the analysis, leading to a guaranteed optimal steadystate behavior.…”

Section: Introductionmentioning

confidence: 99%

“…To resolve the problem of performance deterioration, a modification of the PFKF is proposed on the basis of the analysis, leading to a guaranteed optimal steadystate behavior. The modified version can be seen as an extension of [23] with partitioned-block structure. Numerical simulations are carried out to verify its performance.…”

Section: Introductionmentioning

confidence: 99%

Improving Partition-Block-Based Acoustic Echo Canceler in Under-Modeling Scenarios

Fan¹,

Lü²

2020

Interspeech 2020

Self Cite

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Recently, a partitioned-block-based frequency-domain Kalman filter (PFKF) has been proposed for acoustic echo cancellation. Compared with the normal frequency-domain Kalman filter, the PFKF utilizes the partitioned-block structure, resulting in both fast convergence and low time-latency. We present an analysis of the steady-state behavior of the PFKF and found that it suffers from a biased steady-state solution when the filter is of deficient length. Accordingly, we propose an effective modification that has the benefit of the guaranteed optimal steady-state behavior. Simulations are conducted to validate the improved performance of the proposed method.

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Effective Improvement of Under-Modeling Frequency-Domain Kalman Filter

Cited by 10 publications

References 25 publications

Proton Exchange Membrane Fuel Cell Prognosis Based on Frequency-Domain Kalman Filter

Proton Exchange Membrane Fuel Cell Prognosis Based on Frequency-Domain Kalman Filter

Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet

Improving Partition-Block-Based Acoustic Echo Canceler in Under-Modeling Scenarios

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