DOA estimation based on cross terms of spatial time-frequency distribution matrices

2015 ◽  
Author(s):  
Liu AiJun ◽  
Li Fen ◽  
Chen SiYu ◽  
Yu ChangJun
Keyword(s):  
Frequency Distribution ◽  
Doa Estimation ◽  
Time Frequency ◽  
Cross Terms ◽  
Time Frequency Distribution

Design of a Time-Frequency Distribution for Vibration Monitoring under Corrosions in the Pipe

2006 ◽  
Vol 321-323 ◽  
pp. 1257-1261
Author(s):  
Gi Young Park ◽  
C.K. Lee ◽  
Jung Taek Kim ◽  
K.C. Kwon ◽  
Sang J. Lee
Keyword(s):  
Frequency Distribution ◽  
Vibration Monitoring ◽  
Vibration Signals ◽  
Plant Operation ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Cross Terms ◽  
Conventional Methods ◽  
Time Frequency Distribution

To monitor the wear and degradation on a pipe by corrosion during a plant operation, the vibration signals were measured by an accelerometer and analyzed by several analysis techniques. From the conventional methods, it was difficult to identify the wear and degradation on the pipe. And hence, the time-frequency distribution (TFD) and the adaptive cone-kernel distribution (ACKD) devised for reducing the interfering cross-terms are applied to the acquired data. They can provide the distinguishing peak patterns between the normal and corrosion signals.

scholarly journals Spatial time-frequency distribution of cross term-based direction-of-arrival estimation for weak non-stationary signal

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shuai Shao ◽  
Aijun Liu ◽  
Changjun Yu ◽  
Hongjuan Yang ◽  
Yong Li ◽  
...  
Keyword(s):  
Direction Of Arrival ◽  
Doa Estimation ◽  
Weak Signal ◽  
Weak Signals ◽  
Cross Term ◽  
Time Frequency ◽  
Cross Terms ◽  
The Cross ◽  
Non Stationary Signal ◽  
Time Frequency Distribution

Abstract In the radar array signal processing direction of arrival (DOA), the estimation of weak non-stationary signal is an important and difficult problem when both strong and weak signals are coexisting particularly because the weak non-stationary signals are often submerged in noise. In this paper, we proposed a novelty method to estimate the direction of arrival (DOA) of weak non-stationary signal in scenario for strong non-stationary interference signals and Gaussian white noise. The method utilizes spatial time-frequency distribution (STFD) of cross terms rather than suppressing cross terms in time-frequency analysis. The STFD of cross terms are introduced as an alternative matrix, which is similar to data covariance matrix in multiple signal classification (MUSIC), for the DOA estimation of a weak non-stationary signal. The cross-term amplitude of the strong and weak signals is usually above the noise and is easier to use than the auto-term of the weak signal. In the cross term, the information of the weak signal is included, and the auto-term of these weak signals is difficult to extract directly. The ability to incorporate the STFD of cross terms empowers information about a weak non-stationary signal for DOA estimation, leading to improved signal estimates for direction finding. The method based on the STFD of cross terms for DOA estimation of the weak non-stationary signal is revealed to outperform the time-frequency MUSIC and traditional MUSIC algorithm by simulation, respectively. This method has the advantages of the time-frequency direction finding method and also deals with the situation of weak signals. When the strong and weak signals exist at the same time and the two angles are similar, the cross-terms can be used to perform DOA estimation on the weak signal.

Adaptive Parameters Selection Method for Time-Frequency Distribution Series Based on Normalized Entropy

2013 ◽  
Vol 631-632 ◽  
pp. 1373-1378
Author(s):  
Xiu Li Du ◽  
Ming Ying Liu
Keyword(s):  
Frequency Distribution ◽  
Selection Method ◽  
Gabor Transform ◽  
Adaptive Selection ◽  
Time Frequency ◽  
Window Width ◽  
Parameters Selection ◽  
Cross Terms ◽  
Good Concentration ◽  
Time Frequency Distribution

To resolve the problem of Gabor transform window width and order selection for Time-Frequency Distribution Series (TFDS), a parameters selection method for TFDS based on normalized entropy has been proposed, especially the adaptive selection method of order. The normalized entropy is used to measure the concentration and cross-terms of TFDS firstly, and then the relation between the order and width of Gabor transform window function and the concentration and cross-terms of TFDS is used to realize adaptive selection of window width and order parameter, which overcomes the subjective selection problem of the order. The simulation results show that the proposed method can effectively select optimal TFDS parameters for simulated and experimental ultrasonic tesing signal, and can get TFDS with good concentration and high resolution.

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