DOA Estimation of LFM Signal Based on Single-Source Time-Frequency Points Selection Algorithm by Using the Hough Transform

AbstractTraditional subspace methods which are based on the spatial time-frequency distribution (STFD) matrix have been investigated for direction-of-arrival (DOA) estimation of linear frequency modulation (LFM) signals. However, the DOA estimation performance may degrade substantially when multiple LFM signals are spectrally overlapped in time-frequency (TF) domain. In order to solve this problem, this paper proposes single-source TF points selection algorithm based on Hough transform and short-time Fourier transform (STFT). Firstly, the signal intersections in TF domain can be solved based on the Hough transform, and multiple-source TF points around the intersections are removed, so that the single-source TF points set is reserved. Then, based on the Euclidean distance operator, the single-source TF points set belonging to each signal can be obtained according to the property that TF points of the same signal have same eigenvector. Finally, the averaged STFD matrix is constructed for each signal, and DOA estimation is achieved based on multiple signal classification (MUSIC) algorithm. In this way, the proposed algorithm exhibit remarkable superiority in estimation accuracy and angular resolution over the state-of-the-art schemes and can achieve DOA estimation in the underdetermined cases. In addition, the proposed algorithm can still perform DOA estimation when multiple LFM signals intersect at one point. Numerical simulations demonstrate the validity of the proposed method.

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Two-dimensional DOA estimation algorithm based on time-frequency point selection

Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing - AIIPCC '19 ◽

10.1145/3371425.3371445 ◽

2019 ◽

Author(s):

Yong Hou ◽

Yan Feng ◽

Jie Hao

Keyword(s):

Estimation Algorithm ◽

Doa Estimation ◽

Two Dimensional ◽

Point Selection ◽

Time Frequency

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A radio burst detection method based on the Hough transform

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/staa891 ◽

2020 ◽

Vol 494 (2) ◽

pp. 1994-2003

Author(s):

Shifan Zuo ◽

Xuelei Chen

Keyword(s):

Parameter Space ◽

Radio Burst ◽

Hough Transform ◽

Single Point ◽

Fast Method ◽

Noise Levels ◽

Time Frequency ◽

Straight Line ◽

Fast Radio Burst ◽

Truncation Threshold

ABSTRACT We present a simple and fast method for incoherent dedispersion and fast radio burst (FRB) detection based on the Hough transform, which is widely used for feature extraction in image analysis. The Hough transform maps a point in the time–frequency data to a straight line in the parameter space and points on the same dispersed f−2 curve to a bundle of lines all crossing at the same point, thus the curve is transformed to a single point in the parameter space, enabling an easier way for the detection of radio burst. By choosing an appropriate truncation threshold, in a reasonably radio quiet environment, i.e. with radio frequency interferences present but not dominant, the computing speed of the method is very fast. Using simulation data of different noise levels, we studied how the detected peak varies with different truncation thresholds. We also tested the method with some real pulsar and FRB data.

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Fault Diagnosis Method for Wind Turbine Gearbox Based on Image Characteristics Extraction and Actual Value Negative Selection Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420540348 ◽

2020 ◽

Vol 34 (14) ◽

pp. 2054034

Author(s):

Xiaoli Xu ◽

Xiuli Liu

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Negative Selection ◽

Selection Algorithm ◽

Turbine Generator ◽

Time Frequency ◽

Negative Selection Algorithm ◽

Wind Turbine Generator ◽

Image Characteristics ◽

Diagnosis Method

With the development of information theory and image analysis theory, the studies on fault diagnosis methods based on image processing have become a hot spot in the recent years in the field of fault diagnosis. The gearbox of wind turbine generator is a fault-prone subassembly. Its time frequency of vibration signals contains abundant status information, so this paper proposes a fault diagnosis method based on time-frequency image characteristic extraction and artificial immune algorithm. Firstly, obtain the time-frequency image using wavelet transform based on threshold denoising. Secondly, acquire time-frequency image characteristics by means of Hu invariant moment and correlation fusion gray-level co-occurrence matrix of characteristic value, thus, to extract the fault information of the gearing of wind turbine generator. Lastly, diagnose the fault type using the improved actual-value negative selection algorithm. The application of this method in the gear fault diagnosis on the test bed of wind turbine step-up gearbox proves that it is effective in the improvement of diagnosis accuracy.

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A Joint Method Based on Time-Frequency Distribution to Detect Time-Varying Interferences for GNSS Receivers with a Single Antenna

Sensors ◽

10.3390/s19081946 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1946 ◽

Cited By ~ 1

Author(s):

Qingshui Lv ◽

Honglei Qin

Keyword(s):

Hough Transform ◽

Frequency Estimation ◽

Satellite System ◽

Time Varying ◽

Actual Performance ◽

Time Frequency ◽

Single Antenna ◽

Global Navigation Satellite ◽

Mean Square Errors ◽

Joint Method

In this paper, a joint method combining Hough transform and reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD) is presented to detect time-varying interferences with crossed frequency for a Global Navigation Satellite System (GNSS) receiver with a single antenna. The proposed method can prevent the cross-term interference and detect the time-varying interferences with crossed frequency which cannot be achieved by the classical time-frequency (TF) analysis with the peak detection method. The actual performance of the developed method has been evaluated by experiments with conditions where the real BeiDou system (BDS) B1I signals are corrupted by the simulated chirp interferences. The results of experiments show that the introduced method is effectively able to detect chirp interferences with crossed frequency and provide the same root mean square errors (RMSE) of the parameter estimation for chirp one and the improved initial frequency estimation for chirp two compared with the Hough transform of Wigner-Ville distribution (WVD) when the jamming to noise ratio (JNR) equals or surpasses 4 dB.

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Warped Wigner-Hough Transform for Defect Reflection Enhancement in Ultrasonic Guided Wave Monitoring

Mathematical Problems in Engineering ◽

10.1155/2012/358128 ◽

2012 ◽

Vol 2012 ◽

pp. 1-15 ◽

Cited By ~ 3

Author(s):

Luca De Marchi ◽

Emanuele Baravelli ◽

Giampaolo Cera ◽

Nicolò Speciale ◽

Alessandro Marzani

Keyword(s):

Hough Transform ◽

Guided Waves ◽

Lamb Waves ◽

Guided Wave ◽

Nonlinear Signal Processing ◽

Localization Accuracy ◽

Time Frequency ◽

Ultrasonic Guided Wave ◽

Inspection Systems ◽

Nonlinear Signal

To improve the defect detectability of Lamb wave inspection systems, the application of nonlinear signal processing was investigated. The approach is based on a Warped Frequency Transform (WFT) to compensate the dispersive behavior of ultrasonic guided waves, followed by a Wigner-Ville time-frequency analysis and the Hough Transform to further improve localization accuracy. As a result, an automatic detection procedure to locate defect-induced reflections was demonstrated and successfully tested by analyzing numerically simulated Lamb waves propagating in an aluminum plate. The proposed method is suitable for defect detection and can be easily implemented for real-world structural health monitoring applications.

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