UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework

Wan, Liangtian; Liu, Rong; Sun, Lu; Nie, Hansong; Wang, Xianpeng

doi:10.1016/j.inffus.2021.09.007

Cited by 34 publications

(14 citation statements)

References 24 publications

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“…In the future, the study of pseudolite/UWB integrated method will be focused on the MIMO pseudolite system [36,37], pseudolite/UWB integrated navigation, and deep learning-aided cycle slip detection and repair [38,39]. It is foreseen that the precise positioning results will be benefit to the high accuracy, low-cost indoor localization.…”

Section: Discussionmentioning

confidence: 99%

An Approach to Improve the Indoor Positioning Performance of Pseudolite/UWB System with Ambiguity Resolution

et al. 2022

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Indoor localization is still an open challenge, and some pseudolites have been developed to achieve seamless positioning service based on some commercialized GNSS chips. However, most of these indoor localization technologies often fail in a reasonable solution to the key problems such as low cost and highly accurate and efficient for users. In this paper, we propose an indoor location method based on integrated pseudolite and UWB; the virtual pseudo-range measurements of UWB are used to replace the pseudo-range measurements of pseudolite to solve the indoor multipath problem, which is tightly coupled with the corrected carrier phase measurements of pseudolite. Then, the channel-difference observation equation and UWB-aided ambiguity resolution are proposed for precise positioning. In order to test the proposed method, several experiments are conducted. The results show that the virtual pseudo-range errors from UWB are smaller than that of GNSS, and such a small bias will be better for the fast fixing of ambiguity. In addition, the positioning accuracy of the proposed indoor location method is improved from cm-level for the float solution to mm-level for the fixed solution; these performances would be more convincing to users than that given in the most pseudolite and UWB.

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Section: Discussionmentioning

confidence: 99%

An Approach to Improve the Indoor Positioning Performance of Pseudolite/UWB System with Ambiguity Resolution

et al. 2022

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“…In recent years, deep learning has been widely used in various fields due to its enhanced ability to solve complicated nonlinear problems [14]- [16]. It also provides an advanced solution for conventional DOA estimation.…”

Section: Methodsmentioning

confidence: 99%

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

Huang

Ren

et al. 2022

IEEE Access

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Signal source number detection is an essential issue for the direction of arrival (DOA) estimation in satellite communication systems. The performances of conventional and deep-learning-based signal source number detection methods will deteriorate when the signal-to-noise ratio is low or coherent signals exist. This paper proposes a DOA detection network (DTN) combined with the root weighted subspace fitting (root-WSF) method to tackle this challenge. The DTN uses the constructed deep neural networks (DNN) to denoise the received signals and captures the nonlinear mapping relationship between the received signals and the number of signal sources. The received signals in the complex-valued domain are directly treated as DTN's input, and the label of DTN is the one-hot encoding of the source number. It solves the issue that the classifier cannot well-handle the coherent signals and extends the values of discrete features to Euclidean space. Accordingly, the trained DTN can detect the signal source numbers with an average detection accuracy of 96.6%, and the root-WSF algorithm is applied as the rear stage of DOA estimation. Compared with the traditional DOA methods, the proposed DTN incorporated with the root-WSF algorithm features superior robustness, high DOA estimation accuracy, and enhanced resolution. INDEX TERMS Deep neural network (DNN), direction of arrival (DOA), DOA detection network (DTN), root weighted subspace fitting (root-WSF), satellite communication system.

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“…where Y (i) is the Lagrange multipliers and β > 0 is a penalty parameter. Then we use ADMM to minimize L β iteratively in the following sequence: Firstly, for updating {U (1) , • • • , U (N ) } , it is needed to unfold the tensor (M − µI 1 ) along each mode and then update related factor matrix one by one with the others fixed. So, the optimization is rewritten as follows:…”

Section: Element-wise Average Admmmentioning

confidence: 99%

“…Secondly, for updating A (1) , • • • , A (N ) , the object is to minimize the sum of a matrix trace norm and its Frobenius norm as follows:…”

Section: Element-wise Average Admmmentioning

confidence: 99%

“…

Traditional matrix model of modern communications and radar systems with large-scale terminals has limitations to process the massive volume of signals and has been shifted towards more versatile data analysis tool, namely tensor [1,2]. Tensor provide a natural and compact representation for such multi-ways data, and can be used to express more complicated intrinsic structures in higher-order data [3].

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confidence: 99%

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EA-ADMM: noisy tensor PARAFAC decomposition based on element-wise average ADMM

Yue

Sun

2022

EURASIP J. Adv. Signal Process.

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Tensor decomposition is widely used to exploit the internal correlation in multi-way data analysis and process for communications and radar systems. As one of the main tensor decomposition methods, CANDECOMP/PARAFAC decomposition has advantages of uniqueness and interpretation properties which are significant in practical applications. However, traditional decomposition method is sensitive to both predefined rank and noise that results in inaccurate tensor decomposition. In this paper, we propose a improved algorithm called the Element-wise Average Alternating Direction Method of Multipliers by minimizing the sum of all factors’ trace norm and the noise variance. Our algorithm could overcome the dependence on predefined rank in traditional decomposition algorithms and alleviate the impact of noise. Moreover, this algorithm can be transferred to solve the problem of tensor completion conveniently. The simulation results show that our proposed algorithm could decompose the noisy tensor to the factors with above 90% similarity in various SNR and also interpolate the incomplete tensor with higher similar coefficient and lower relative reconstruction error when the missing rate is less than 0.5.

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UAV swarm based radar signal sorting via multi-source data fusion: A deep transfer learning framework

Cited by 34 publications

References 24 publications

An Approach to Improve the Indoor Positioning Performance of Pseudolite/UWB System with Ambiguity Resolution

An Approach to Improve the Indoor Positioning Performance of Pseudolite/UWB System with Ambiguity Resolution

Robust DOA Estimation in Satellite Systems in Presence of Coherent Signals Subject to Low SNR

EA-ADMM: noisy tensor PARAFAC decomposition based on element-wise average ADMM

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