Modeling and Error Accuracy Analysis of Three Satellite Interference Source Location System by Low and High Orbit

Shuai, Haifeng; Zhu, Shibing; Li, Changqing

doi:10.1109/iceiec.2019.8784590

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…However, it is difficult to detect the signal source number under the scenario r < K, where coherent signals exist. Radio frequency interference (RFI) incidents from the ground to satellites show an upward trend [28], [29]. Therefore, robust DOA estimation techniques should be considered for array signal processing in LEOP and low earth orbit (LEO) satellites.…”

Section: [•]mentioning

confidence: 99%

“…W ITH the development of satellite communication systems, radio spectrum resources have become scarce [1]. This causes interference on the earth's surface as well as in space, which poses massive threats to regular satellite communications [2], [3]. Hence, the array signal processing has received even more attention in recent years, and the direction of arrival (DOA) is one of the essential topics in the field of array signal processing.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: [•]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Huang

Ren

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Especially under certain scenarios (e.g., launch and early orbit phase (LEOP) of critical maneuvers), ground stations will lose the ability to operate the spacecraft [2]. Besides, radio frequency interference (RFI) incidents from the ground to satellites show an upward trend [3], [4]. Therefore, for array signal processing in LEOP and low earth orbit (LEO) satellites, reliable and fast direction of arrival (DOA) estimation techniques should be considered.…”

Section: Introductionmentioning

confidence: 99%

Recurrent NEAT Assisted 2D-DOA Estimation With Reduced Complexity for Satellite Communication Systems

Huang

Pedersen

et al. 2022

IEEE Access

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Direction-of-arrival (DOA) estimation plays a vital role in the field of array signal processing. However, the need for heavy computing tasks in most traditional DOA algorithms, e.g., multiple signal classification (MUSIC), makes their engineering practicality significantly compromised in satellite communication systems. The neuroevolution of augmenting topologies (NEAT) can quickly search for appropriate topologies and weights of neural network functions, but its computational complexity is still too high for satellite systems. This paper proposes a modified NEAT architecture featuring a recurrent structure (RNEAT) that only needs a small number of phase components of the received signal covariance matrix as inputs to reduce the complexity and simplify the neural network architecture. The proposed RNEAT incorporated with multiple signal classification (RNEAT-MUSIC) can be easy to achieve high resolution and low complexity simultaneously. Validation has been done by applying the proposed method in a twodimensional direction of arrival estimation (2D-DOA) problem. Results show that the proposed RNEAT-MUSIC efficiently restricts the scanning region before forwarding the covariance matrix to the MUSIC stage. Consequently, the computational workload is reduced by 3/4 compared with the traditional 2D-MUSIC algorithm while maintaining good DoA resolution.INDEX TERMS Direction-of-arrival (DOA), neuroevolution of augmenting topologies (NEAT), multiple signal classification (MUSIC), RNEAT-MUSIC, satellite communication.

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An Improved Variable Step Size LMSTDE Algorithm Based on Fourth-order Cumulant

Liao

Liu

2021

2021 7th International Conference on Computer and Communications (ICCC)

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Modeling and Error Accuracy Analysis of Three Satellite Interference Source Location System by Low and High Orbit

Cited by 4 publications

References 9 publications

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

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

Recurrent NEAT Assisted 2D-DOA Estimation With Reduced Complexity for Satellite Communication Systems

An Improved Variable Step Size LMSTDE Algorithm Based on Fourth-order Cumulant

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