High-resolution direction finding of non-stationary signals using matching pursuit

Ghofrani, Sedigheh; Amin, Moeness G.; Zhang, Yimin D.

doi:10.1016/j.sigpro.2013.03.016

Cited by 25 publications

(25 citation statements)

References 24 publications

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“…In the case of electromagnetic waves with firing frequencies of 1 f and 2 f , the interval is very short, in microseconds, the distance of the nodes moving is much less than one wavelength, so it can be assumed that the numbers of integer multiple wavelengths in the two phases are the same [8][9] .Equation (5) …”

Section: Clock Synchronizationmentioning

confidence: 99%

Residual correction WLS based on Indoor Location Algorithm Considering Arrival Angle and Phase Difference Parameter Estimation

2017

2017 5th International Conference on Computer, Automation and Power Electronics (CAPE 2017)

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Section: Clock Synchronizationmentioning

confidence: 99%

Residual correction WLS based on Indoor Location Algorithm Considering Arrival Angle and Phase Difference Parameter Estimation

2017

2017 5th International Conference on Computer, Automation and Power Electronics (CAPE 2017)

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“…TFDs for a large class of FM signals are commonly obtained by linear basis signal decomposition, 14,15 or quadratic time-frequency distribution, generally referred as Cohen's class.…”

Section: -13mentioning

confidence: 99%

Time-frequency signature sparse reconstruction using chirp dictionary

Nguyen

Amin

Ghogho

et al. 2015

Compressive Sensing IV

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This paper considers local sparse reconstruction of time-frequency signatures of windowed non-stationary radar returns. These signals can be considered instantaneously narrow-band, thus the local time-frequency behavior can be recovered accurately with incomplete observations. The typically employed sinusoidal dictionary induces competing requirements on window length. It confronts converse requests on the number of measurements for exact recovery, and sparsity. In this paper, we use chirp dictionary for each window position to determine the signal instantaneous frequency laws. This approach can considerably mitigate the problems of sinusoidal dictionary, and enable the utilization of longer windows for accurate time-frequency representations. It also reduces the picket fence by introducing a new factor, the chirp rate α. Simulation examples are provided, demonstrating the superior performance of local chirp dictionary over its sinusoidal counterpart.

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“…Hence, the selection of a high resolution TFD is of fundamental importance for the DOA estimation of closely separated sources in the TFD domain. The matching pursuit based signal decomposition algorithm can be used to obtain a cross-term free high resolution TFD, which can be then exploited to estimate DOA (Ghofrani et al 2013). However, the matching pursuit based DOA estimation method assumes that t-f dictionaries are a prior known (Mallat and Zhang 1993).…”

Section: Introductionmentioning

confidence: 99%

Direction of arrival estimation using adaptive directional time-frequency distributions

Khan

Ali

Jansson

2016

Multidim Syst Sign Process

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Time-frequency distributions (TFDs) allow direction of arrival (DOA) estimation algorithms to be used in scenarios when the total number of sources are more than the number of sensors. The performance of such time-frequency (t-f) based DOA estimation algorithms depends on the resolution of the underlying TFD as a higher resolution TFD leads to better separation of sources in the t-f domain. This paper presents a novel DOA estimation algorithm that uses the adaptive directional t-f distribution (ADTFD) for the analysis of close signal components. The ADTFD optimizes the direction of kernel at each point in the t-f domain to obtain a clear t-f representation, which is then exploited for DOA estimation. Moreover, the proposed methodology can also be applied for DOA estimation of sparse signals. Experimental results indicate that the proposed DOA algorithm based on the ADTFD outperforms other fixed and adaptive kernel based DOA algorithms.

show abstract

High-resolution direction finding of non-stationary signals using matching pursuit

Cited by 25 publications

References 24 publications

Residual correction WLS based on Indoor Location Algorithm Considering Arrival Angle and Phase Difference Parameter Estimation

Residual correction WLS based on Indoor Location Algorithm Considering Arrival Angle and Phase Difference Parameter Estimation

Time-frequency signature sparse reconstruction using chirp dictionary

Direction of arrival estimation using adaptive directional time-frequency distributions

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