Autonomous wireless radar sensor mote for target material classification

Khan, Muhammad Murtaza; Iftekharuddin, Khan M.; McCracken, Ernest; Islam, Khandakar; Bhurtel, Sushil; Wang, Lan; Kozma, Róbert

doi:10.1016/j.dsp.2012.11.010

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…Estimation of the source number from noisy received data is a fundamental problem in array signal processing and related applications, such as radar [1], acoustic signal processing [2], wireless communications [3], Internet of Things (IoT) [4], etc. In particular, most direction-of-arrival (DOA) estimation algorithms (e.g., MUSIC and ESPRIT [5]) rely on the prior knowledge o accurate source number and would fail when such knowledge is unavailable.…”

Section: Introductionmentioning

confidence: 99%

Model-Aided Deep Neural Network for Source Number Detection

Yang

Gao

Cheng

et al. 2020

IEEE Signal Process. Lett.

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Source number detection is a critical problem in array signal processing. Conventional model-driven methods e.g., Akaikes information criterion (AIC) and minimum description length (MDL), suffer from severe performance degradation when the number of snapshots is small or the signal-to-noise ratio (SNR) is low. In this paper, we exploit the model-aided based deep neural network (DNN) to estimate the source number. Specifically, we first propose the eigenvalue based regression network (ERNet) and classification network (ECNet) to estimate the number of non-coherent sources, where the eigenvalues of the received signal covariance matrix and the source number are used as the input and the supervise label of the networks, respectively. Then, we extend the ERNet and ECNet for estimating the number of coherent sources, where the forward-backward spatial smoothing (FBSS) scheme is adopted to improve the performance of ERNet and ECNet. Numerical results demonstrate the outstanding performance of ERNet and ECNet over the conventional AIC and MDL methods as well as their excellent generalization capability, which also shows their great potentials for practical applications.

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Section: Introductionmentioning

confidence: 99%

Model-Aided Deep Neural Network for Source Number Detection

Yang

Gao

Cheng

et al. 2020

IEEE Signal Process. Lett.

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“…Detecting wideband source number is one of the hot and difficult problems in the area of array signal processing; meanwhile, it is widely used in many fields, such as radio monitoring [1][2][3][4][5], acoustic signal processing [6][7][8][9][10], and Internet of Things [11][12][13][14]; the corresponding methods are also being studied by a lot of scholars all the time. Generally speaking, knowing number of signals is also the premier condition of estimating direction of arrival (DOA).…”

Section: Introductionmentioning

confidence: 99%

Detection of Wideband Signal Number Based on Bootstrap Resampling

Zhen

2016

International Journal of Antennas and Propagation

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Knowing source number correctly is the precondition for most spatial spectrum estimation methods; however, many snapshots are needed when we determine number of wideband signals. Therefore, a new method based on Bootstrap resampling is proposed in this paper. First, signals are divided into some nonoverlapping subbands; apply coherent signal methods (CSM) to focus them on the single frequency. Then, fuse the eigenvalues with the corresponding eigenvectors of the focused covariance matrix. Subsequently, use Bootstrap to construct the new resampling matrix. Finally, the number of wideband signals can be calculated with obtained vector sequences according to clustering technique. The method has a high probability of success under low signal to noise ratio (SNR) and small number of snapshots.

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