Source Enumeration Based on a Uniform Circular Array in a Determined Case

Mei, Chao; Tian, Nili; Ling, Bingo Wing‐Kuen; Wang, Everett X.

doi:10.1109/tvt.2018.2884522

Cited by 11 publications

(15 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…If the estimated quantity is different from the actual one, the DOA estimation will be affected. Pan et al [56] proposed a source number enumeration model (M-UCA) of UCA with M antennas.…”

Section: Signal Source Number Estimationmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

View full text Add to dashboard Cite

In array signal processing, the direction of arrival (DOA) of the signal source has drawn broad research interests with its wide applications in fields such as sonar, radar, communications, medical detection, and electronic countermeasures. In recent years, the application of deep learning (DL) to DOA estimation has achieved great success. This study provides a systematic review of research on DOA estimation using deep neural network methods. We manually selected twenty-five papers related to this research from five prominent databases (SpringerLink, IEEE Xplore, ScienceDirect, Scopus, and Google Scholar) for exploration. Six questions describing the overall trend of DOA estimation using deep learning are put forward. Then, we answered these questions by reviewing the literature. This review is helpful for researchers in this field because it provides more specific and comprehensive information needed for future research. Specifically, we first analyzed the background of the selected papers, including the type of publication, the number of citations, and the country of origin. Then, the DL technology used in DOA estimation is systematically analyzed, including the purpose of using DL in DOA estimation, various DL models (convolutional neural network, deep neural network, and combination network), and various DOA estimation schemes. Finally, various evaluation criteria (root-mean-squared error, accuracy, and mean absolute error) are used to evaluate the DL technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas, and number of signal sources) affecting DOA estimation are analyzed. Based on our findings, we believe that deep learning can perform DOA estimation well, and there is still room for improvement in deep learning technology. In this study, the factors affecting DOA estimation can be used as the direction for researchers to conduct in-depth research.

show abstract

“…If the estimated quantity is different from the actual one, the DOA estimation will be affected. Pan et al [56] proposed a source number enumeration model (M-UCA) of UCA with M antennas.…”

Section: Signal Source Number Estimationmentioning

confidence: 99%

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Li²,

Rum

2021

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Figure 7 shows the estimation accuracy of AIC, MDL, SORTE, and our two proposed approaches (AREG, T-GANE) versus SNR. The performances are evaluated in the SNR range from −20 dB to 10 dB, which is roughly chosen in many other papers [4,[10][11][12]14,16,[23][24][25]31]. This paper is interested in improving accuracy of AIC at high SNR-where MDL has 100% accuracy, but AIC does not reach 100% accuracy-and it of MDL at low SNR-where the MDL accuracy begins to decrease sharply, but AIC maintais good accuracy.…”

Section: Evaluation Of Comprehensive Approachesmentioning

confidence: 99%

“…In practice, however, the number of sources is not known a priori; the source enumeration must be executed before the DOA techniques are performed. If we fail to estimate the exact number of sources, it will lead to a deterioration in the performance of the DOA estimation [11]. Therefore, the source enumeration is greatly important for DOA estimation.…”

Section: Introductionmentioning

confidence: 99%

Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

et al. 2021

View full text Add to dashboard Cite

Source enumeration is an important procedure for radio direction-of-arrival finding in the multiple signal classification (MUSIC) algorithm. The most widely used source enumeration approaches are based on the eigenvalues themselves of the covariance matrix obtained from the received signal. However, they have shortcomings such as the imperfect accuracy even at a high signal-to-noise ratio (SNR), the poor performance at low SNR, and the limited detection number of sources. This paper proposestwo source enumeration approaches using the ratio of eigenvalue gaps and the threshold trained by a machine learning based clustering algorithm for gaps of normalized eigenvalues, respectively. In the first approach, a criterion formula derived with eigenvalue gaps is used to determine the number of sources, where the formula has maximum value. In the second approach, datasets of normalized eigenvalue gaps are generated for the machine learning based clustering algorithm and the optimal threshold for estimation of the number of sources are derived, which minimizes source enumeration error probability. Simulation results show that our proposed approaches are superior to the conventional approaches from both the estimation accuracy and numerical detectability extent points of view. The results demonstrate that the second proposed approach has the feasibility to improve source enumeration performance if appropriate learning datasets are sufficiently provided.

show abstract

“…(4) Update speed and position of each particle according to (34) and (35). (5) Compare the current value with the former one to obtain the individual optimal value p d i , then compare the present global optimum with the historical optimum to get global optimal position.…”

Section: Parameter Initializationmentioning

confidence: 99%

“…SVM transforms the problem of nonlinear classification into linear classification through increasing the data dimension, it shows good generalization ability, solves the common overfitting in traditional algorithms, the efficiency and accuracy of the algorithm are relatively high. At present, SVM has been put into text classification, biomedicine, face recognition, and signal number estimation [34][35], simultaneously more and more researchers solve these problems by neural network [36][37], but they are merely suitable for uncorrelated sources and Gaussian white noise.…”

Section: Introductionmentioning

confidence: 99%

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Zhen

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

Knowing number of nodes is the precondition of their locating and estimation of other parameters in wireless sensor network, actually it can be evaluated according to the signal received by the sensor array. The popular algorithms are only appropriate for the uncorrelated signals and circumstance of Gaussian white noise, the estimation precision will deteriorate at some harsh environments. Hence a new algorithm for determining coherent node number based on multiple feature extraction in wireless sensor network is presented in this paper. First, perform spatial difference smoothing to the array data. Then the eigenvalues and eigenvectors can be acquired by eigen-decomposition, consequently multiple features which are used for training are constructed. Finally, back propagation neural network and particle swarm optimization are exploited for calculating node number. Simulation results demonstrate that the algorithms has good performances under the background of colored noise and small samples.

show abstract

Source Enumeration Based on a Uniform Circular Array in a Determined Case

Cited by 11 publications

References 34 publications

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

Source Enumeration Approaches Using Eigenvalue Gaps and Machine Learning Based Threshold for Direction-of-Arrival Estimation

Number of Coherent Nodes Estimation Based on Multiple Feature Extraction in Wireless Sensor Network

Contact Info

Product

Resources

About