Time Difference of Arrival Estimation Based on a Kronecker Product Decomposition

Wang, Xianrui; Huang, Gongping; Benesty, Jacob; Chen, Jingdong; Cohen, Israel

doi:10.1109/lsp.2020.3044775

Cited by 29 publications

(4 citation statements)

References 26 publications

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“…In [23], the authors showed that an acoustic impulse response can be well modeled using a summed Kronecker structure, thereby significantly reducing the number of parameters required for the detail ofthe channel. These efforts have since been extended to incorporating such a structure in a variety of estimators [24,25], including both an RLS-based [26] and an LMS-based estimator [27]. To further this development, this work presents a joint estimation framework for time-varying channels, which is robust against the low SNR scenarios typically encountered in underwater measurements, as well as a detection algorithm for weak underwater targets, for active sonar systems.…”

Section: Introductionmentioning

confidence: 99%

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses

Yang,

Ling,

Sheng

et al. 2024

Remote Sensing

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In this paper, we considered the real-time modeling of an underwater channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development in the modeling of acoustic channels using a Kronecker structure, we approximated the CIR using a structured and sparse model, allowing for a computationally efficient sparse block-updating algorithm, which can track the time-varying CIR even in low signal-to-noise ratio (SNR) scenarios. The algorithm employs a conjugate gradient formulation, which enables a gradual refinement if the SNR is sufficiently high to allow for this. This was performed by gradually relaxing the assumed Kronecker structure, as well as the sparsity assumptions, if possible. The estimated CIR was further used to form a residual signal containing (primarily) information of the time-varying signal responses, thereby allowing for the detection of weak target signals. The proposed method was evaluated using both simulated and measured underwater signals, clearly illustrating the better performance of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses

Yang,

Ling,

Sheng

et al. 2024

Remote Sensing

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“…Typically, a TD estimate is obtained by the generalized cross correlation (GCC) method [11], [12]. Any improvement in TD estimation (TDE) directly benefits the subsequent applications, and numerous studies on this subject have been carried out [1], [12]- [16].…”

Section: Introductionmentioning

confidence: 99%

Minimum-Spanning-Tree-Based Time Delay Estimation Robust to Outliers

Yamaoka,

Nakashima,

Wakabayashi

et al. 2023

IEEE Access

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In this paper, we present a novel approach to estimating multiple time delays (TDs) in sensor arrays that is robust to outliers of TD measurements. These measurements are typically obtained from the peak of the cross correlation of two sensor signals but may contain outliers due to noise, significantly degrading the performance of downstream applications. To address this issue, we propose an approach to leverage only the best minimum-necessary TD measurements. First, we describe the general properties of TDs and show that the degree of freedom of TDs is the number of sensors minus one for a signal source, which indicates that the full set of TDs is redundant in this case. We then consider selecting nonredundant TDs from all measurements given the necessary and sufficient condition to reconstruct all TDs uniquely. We represent this condition by utilizing the graph theory, and then, formulate an optimization problem to select the optimal nonredundant TDs while satisfying the condition above. We reduce this problem to the problem of finding a minimum spanning tree and propose an efficient algorithm for TD estimation. Experimental evaluation shows that our method successfully eliminates outliers while ensuring that all TDs can be restored.

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“…Furthermore, by using the decomposition based on the nearest Kronecker product, followed by low-rank approximations, more general forms of impulse responses can be identified, which are not linearly separable [16][17][18][19]. Recent studies on this topic have targeted specific applications, like echo cancellation, noise reduction, adaptive beamforming, microphone arrays, linear prediction, and speech dereverberation, e.g., see [20][21][22][23][24][25][26][27][28] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Efficient Algorithms for Linear System Identification with Particular Symmetric Filters

et al. 2022

Self Cite

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In linear system identification problems, it is important to reveal and exploit any specific intrinsic characteristic of the impulse responses, in order to improve the overall performance, especially in terms of the accuracy and complexity of the solution. In this paper, we focus on the nearest Kronecker product decomposition of the impulse responses, together with low-rank approximations. Such an approach is suitable for the identification of a wide range of real-world systems. Most importantly, we reformulate the system identification problem by using a particular symmetric filter within the development, which allows us to efficiently design two (iterative/recursive) algorithms. First, an iterative Wiener filter is proposed, with improved performance as compared to the conventional Wiener filter, especially in challenging conditions (e.g., small amount of available data and/or noisy environments). Second, an even more practical solution is developed, in the form of a recursive least-squares adaptive algorithm, which could represent an appealing choice in real-time applications. Overall, based on the proposed approach, a system identification problem that can be conventionally solved by using a system of L=L1L2 equations (with L unknown parameters) is reformulated as a combination of two systems of PL1 and PL2 equations, respectively, where usually P≪L2 (i.e., a total of PL1+PL2 parameters). This could lead to important advantages, in terms of both performance and complexity. Simulation results are provided in the framework of network and acoustic echo cancellation, supporting the performance gain and the practical features of the proposed algorithms.

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Time Difference of Arrival Estimation Based on a Kronecker Product Decomposition

Cited by 29 publications

References 26 publications

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses

Detecting Weak Underwater Targets Using Block Updating of Sparse and Structured Channel Impulse Responses

Minimum-Spanning-Tree-Based Time Delay Estimation Robust to Outliers

Efficient Algorithms for Linear System Identification with Particular Symmetric Filters

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