This letter presents a state estimation algorithm for linear discrete-time systems with state-delay. In order to overcome the difficulty that the traditional Kalman filter cannot estimate the states of the systems wit...
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This letter presents a state estimation algorithm for linear discrete-time systems with state-delay. In order to overcome the difficulty that the traditional Kalman filter cannot estimate the states of the systems with state-delay, a state estimation strategy is developed by combining the auxiliary model with the delta operator. Then, by constructing and minimizing the covariance matrix of the state reconstruction errors, a delta operator state estimation algorithm is derived and it can fulfill effective state estimation for the linear discrete-time system with state-delay. Moreover, the convergence proof is provided by means of stochastic stability theory. Finally, the experimental results demonstrate that the developed state estimation method is effective.
This paper presents a sound event detection (SED) method that handles sound event boundaries in a statistically principled manner. A typical approach to SED is to train a deep neural network (DNN) in a supervised mann...
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This paper presents a sound event detection (SED) method that handles sound event boundaries in a statistically principled manner. A typical approach to SED is to train a deep neural network (DNN) in a supervised manner such that the model predicts frame-wise event activities. Since the predicted activities often contain fine insertion and deletion errors due to their temporal fluctuations, post-processing has been applied to obtain more accurate onset and offset boundaries. Existing post-processing methods are, however, non-differentiable and prohibit end-to-end (E2E) training. In this paper, we propose an E2E detection method based on a probabilistic formulation of sound event sequences called a hidden semi-Markov model (HSMM). The HSMM is utilized to transform frame-wise features predicted by a DNN into posterior probabilities of sound events represented by their class labels and temporal boundaries. We jointly train the DNN and HSMM in a supervised E2E manner by maximizing the event-wise posterior probabilities of the HSMM. This objective is a differentiable function thanks to the forward-backward algorithm of the HSMM. Experimental results with real recordings show that our method outperforms baseline systems with standard post-processing methods.
A low-complexity wavenumber-domain positioning method is proposed for near-field sensing. Specifically, in the wavenumber domain, the power-concentrated region is sparse and closely related to the target's positio...
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A low-complexity wavenumber-domain positioning method is proposed for near-field sensing. Specifically, in the wavenumber domain, the power-concentrated region is sparse and closely related to the target's position. However, this relationship is complex and implicit. To address this, a bi-directional convolutional neural network (BiCNN) architecture is employed to capture the underlying relationship, enabling low-complexity, gridless target positioning. The simulation results reveal that the BiCNN method significantly reduces the computational complexity compared to the existing on-grid multiple signal classification (MUSIC) algorithm while achieving high accuracy.
In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as mul...
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In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.
Adaptive filters utilizing the low-order moments hidden in robust loss functions have achieved desirable performance under Gaussian input and impulsive noises. However, when the input cannot be modeled by Gaussian pro...
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Adaptive filters utilizing the low-order moments hidden in robust loss functions have achieved desirable performance under Gaussian input and impulsive noises. However, when the input cannot be modeled by Gaussian process and is simultaneously contaminated by outliers, these filters may suffer from misalignment. To this end, applying fractional-order calculus in stochastic gradient descent method, this letter proposes a fractional-order generalized Cauchy kernel loss (FoGCKL) algorithm to model complex alpha-stable process input. The mean square deviation (MSD) is calculated to evaluate the steady-state performance of FoGCKL. To further avoid steady-state jitters and improve filtering accuracy, an enhanced batch method is constructed in FoGCKL using optimized weighted term, generating another enhanced batch FoGCKL (EBFoGCKL) algorithm. Simulations on system identification verify the correctness of theoretical analysis and demonstrate the superiorities of FoGCKL and EB-FoGCKL.
The future machine-type communication in internet-of-things (IoT) systems involves a massive number of devices sporadically communicating with a base station (BS) equipped with multiple antennas. Detecting active devi...
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The future machine-type communication in internet-of-things (IoT) systems involves a massive number of devices sporadically communicating with a base station (BS) equipped with multiple antennas. Detecting active devices and estimating their associated channels are crucial but challenging due to the large number of potential devices and the small fraction of active devices. Existing studies assume high-resolution analog-to-digital converters (ADCs) at the BS, while there is a growing interest in implementing low-resolution ADCs, particularly one-bit ADCs, in massive multiple-input multiple-output (MIMO) systems. This paper focuses on the joint one-bit active device detection and channel estimation problem. We consider the maximum-likelihood approach and propose a novel expectation maximization (EM) algorithm with acceleration. On the theoretical aspect, we provide the convergent computational complexity analysis for the accelerated EM algorithm. The proposed method, evaluated through numerical simulations, outperforms benchmark algorithms in terms of both estimation accuracy and computational complexity.
The Steady State Visual Evoked Potential (SSVEP) paradigm has been widely employed in various Brain-Computer Interface (BCI) systems. However, recent studies indicate that SSVEP is vulnerable to adversarial attacks, r...
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The Steady State Visual Evoked Potential (SSVEP) paradigm has been widely employed in various Brain-Computer Interface (BCI) systems. However, recent studies indicate that SSVEP is vulnerable to adversarial attacks, resulting in manipulated results and drastic degradation in recognition performance, which pose inconveniences and even risks to users. Noticing the fact that the adversarial attack on SSVEP is done by adding subtle waveform perturbations into random EEG channels, we propose Independent Components Time-Frequency Purification with Channel Consensus (ICTFP-CC) as a defensive strategy. In particular, we first detect and remove suspicious perturbations with independent component analysis from the time and frequency domain, and then reconstruct the purified EEG signals. Additionally, we introduce a voting mechanism to achieve channel consensus and enhance overall robustness. We conducted experiments on two public datasets and three SSVEP recognition algorithms. The results demonstrate that our method can significantly improve the classification accuracy and information transfer rate of attacked SSVEP signals by a maximum of 46.79 (%) and 62.87 (bits/min).
The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this letter, we consider unlimited sensing fram...
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The success of full-stack full-duplex communication systems depends on how effectively one can achieve digital self-interference cancellation (SIC). Towards this end, in this letter, we consider unlimited sensing framework (USF) enabled full-duplex system. We show that by injecting folding non-linearities in the sensing pipeline, one can not only suppress self-interference but also recover the signal of interest (SoI). This approach leads to novel design of the receiver architecture that is complemented by a modulo-domain channel estimation method. We then demonstrate the advantages of M-lambda -ADC by analyzing the relationship between quantization noise, quantization bits, and dynamic range. Numerical experiments show that the USF enabled receiver structure can achieve up to 40 dB digital SIC by using as few as 4-bits per sample. Our method outperforms the previous approach based on adaptive filters when it comes to digital SIC performance, SoI reconstruction, and detection.
The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance deg...
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The principal-singular-vector utilization for modal analysis (PUMA) and its modification (Mod-PUMA), which utilize forward linear prediction (FLP) to process the signal subspace, experience significant performance degradation if there are multiple coherent sources and such a performance degradation will be further aggravated in low-SNR regions, which is primarily attributed to the outliers arising from inaccurate estimations of the signal subspace. To address these issues, we propose an extension version of PUMA-related algorithms, called FBLP-Mod-PUMA/enhanced-PUMA (EPUMA). The proposed algorithms improve the threshold performance by refining the signal subspace through forward and backward linear prediction (FBLP), effectively mitigating subspace leakage when dealing with coherent sources. The number of resolvable coherent sources has been theoretically derived and simulation results are provided to show the performance of the proposed algorithms.
The letter addresses the robust state estimation problem of non-Gaussian systems disturbed by outliers. Unlike the existing correntropy-based state estimation framework, which uses a uniform weight for the evaluated e...
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The letter addresses the robust state estimation problem of non-Gaussian systems disturbed by outliers. Unlike the existing correntropy-based state estimation framework, which uses a uniform weight for the evaluated error vectors and solely relies on inaccurate nominal covariance matrices for estimating the system state, this work proposes a novel maximum-correntropy Kalman filter. This new approach utilizes multiple dimensional correntropy to assess the similarity between vectors across different dimensions. Additionally, it adjusts the covariance matrices simultaneously by utilizing the adopted matrix similarity measure within the modified correntropy framework. Simulations on target tracking demonstrate that our proposed algorithm exhibits excellent estimation accuracy and robustness while possessing adaptive capability even under time-varying heavy-tailed noises.
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