Joint Estimation of Time-Frequency Signature and DOA Based on STFD for Multicomponent Chirp Signals

In the study of the joint estimation of time-frequency signature and direction of arrival (DOA) for multicomponent chirp signals, an estimation method based on spatial time-frequency distributions (STFDs) is proposed in this paper. Firstly, array signal model for multicomponent chirp signals is presented and then array processing is applied in time-frequency analysis to mitigate cross-terms. According to the results of the array processing, Hough transform is performed and the estimation of time-frequency signature is obtained. Subsequently, subspace method for DOA estimation based on STFD matrix is achieved. Simulation results demonstrate the validity of the proposed method.

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Subspace array processing using spatial time-frequency distributions: Applications for denoising structural echoes of elastic targets

The Journal of the Acoustical Society of America ◽

10.1121/1.4871183 ◽

2014 ◽

Vol 135 (5) ◽

pp. 2821-2835 ◽

Cited By ~ 5

Author(s):

Karim G. Sabra ◽

Shaun D. Anderson

Keyword(s):

Array Processing ◽

Frequency Distributions ◽

Time Frequency

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Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

10.21203/rs.3.rs-278792/v1 ◽

2021 ◽

Author(s):

Di Zhao ◽

Weijie Tan ◽

Zhongliang Deng ◽

Gang Li

Keyword(s):

Optimization Problem ◽

Signal To Noise Ratio ◽

Estimation Method ◽

Doa Estimation ◽

Low Complexity ◽

Estimation Methods ◽

Circular Array ◽

Single Measurement ◽

Signal Model ◽

Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

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Robust spatial time-frequency distributions for DOA estimation using spatial averaging and directional smoothing

Signal Processing ◽

10.1016/j.sigpro.2020.107897 ◽

2021 ◽

Vol 180 ◽

pp. 107897

Author(s):

Nabeel Ali Khan ◽

Sadiq Ali

Keyword(s):

Doa Estimation ◽

Spatial Averaging ◽

Frequency Distributions ◽

Time Frequency

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Robust Watermarking Algorithm Based on Time-Frequency CHIRPLET

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.179-180.881 ◽

2011 ◽

Vol 179-180 ◽

pp. 881-885

Author(s):

Ming Hui Deng ◽

Qing Shuang Zeng ◽

Yu Song

Keyword(s):

Image Watermarking ◽

Time Windows ◽

Geometric Distortion ◽

Frequency Change ◽

Two Dimensional ◽

Frequency Distributions ◽

Time Frequency ◽

Chirp Signals ◽

Wigner Transformation ◽

Frequency Properties

In this paper, a robust image watermarking method in two-dimensional time-frequency distributions domain is proposed which is robust against geometric distortion. This watermarking is detected by a linear frequency change. The chirplet transformation is used to detect the watermark. The chirp signals are used as watermarks and this type of signals is resistant to all stationary filtering methods and exhibits geometrical symmetry. In the two-dimensional Radon-Wigner transformation domain, the chirp signals used as watermarks change only its position in space/spatial-frequency distribution, after applying linear geometrical attack, such as scale rotation and cropping. But the two-dimensional Radon-Wigner transformation needs too much difficult computing. So the image is put into a series of 1D signal by choosing scalable local time windows. The watermark embedded in the chirplet transformation domain. The watermark thus generated is invisible and performs well in StirMark test and is robust to geometrical attacks. Compared with other watermarking algorithms, this algorithm is more robust, especially against geometric distortion, while having excellent frequency properties.

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An Improved Direction-of-Arrival Estimation Method Based on Sparse Reconstruction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.135 ◽

2012 ◽

Vol 263-266 ◽

pp. 135-138

Author(s):

Xue Bing Han ◽

Zhao Jun Jiang

Keyword(s):

Estimation Method ◽

Direction Of Arrival ◽

Doa Estimation ◽

Sparse Reconstruction ◽

Direction Of Arrival Estimation ◽

Sensing Matrix ◽

Signal Model ◽

Multiple Measurement Vectors ◽

Underdetermined System ◽

Simulation Results

In this paper, we account for efficient approach of direction-of-arrival estimation based on sparse reconstruction of sensor measurements with an overcomplete basis. MSD-FOCUSS ( MMV Synchronous Descending FOCal Underdetermined System Solver) algorithm is developed against to sparse reconstruction in multiple-measurement-vectors (MMV) system where noise perturbations exist in both the measurements and sensing matrix. The paper shows how sparse-signal model of DOA estimation is established and MSD-FOCUSS is derived, then the simulation results illustrate the advantage of MSD-FOCUSS when it is used to solve the problem of DOA estimation.

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