A spectral estimation algorithm using the householder transform

Due to the large distance between transmitting array and receiving array in bistatic MIMO, The bistatic MIMO is unable to use the waveform diversity technology base on the monostatic MIMO to improve the resolution of the detection. And bistatic MIMO angle estimation algorithm based on the spatial spectral estimation is unable to get coherent gain from beamforming. In this paper, position calculating of the bistatic sonar is used to get the relationship among DOD DOA and propagation distance. The array manifold of virtual array is constructed in each scan position in 2-d imaging. The improved waveform diversity technology is used in the imaging of bistatic MIMO to improve the resolution of the detection.

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Multi-Modal, Remote Breathing Monitor

Sensors ◽

10.3390/s20041229 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1229 ◽

Cited By ~ 1

Author(s):

Nir Regev ◽

Dov Wulich

Keyword(s):

Human Subjects ◽

Elderly Care ◽

Spectral Estimation ◽

Harmonic Signal ◽

Estimation Algorithm ◽

Maximum Error ◽

Ultra Wideband ◽

Ratio Test ◽

Breathing Rate ◽

Set Up

Monitoring breathing is important for a plethora of applications including, but not limited to, baby monitoring, sleep monitoring, and elderly care. This paper presents a way to fuse both vision-based and RF-based modalities for the task of estimating the breathing rate of a human. The modalities used are the F200 Intel® RealSenseTM RGB and depth (RGBD) sensor, and an ultra-wideband (UWB) radar. RGB image-based features and their corresponding image coordinates are detected on the human body and are tracked using the famous optical flow algorithm of Lucas and Kanade. The depth at these coordinates is also tracked. The synced-radar received signal is processed to extract the breathing pattern. All of these signals are then passed to a harmonic signal detector which is based on a generalized likelihood ratio test. Finally, a spectral estimation algorithm based on the reformed Pisarenko algorithm tracks the breathing fundamental frequencies in real-time, which are then fused into a one optimal breathing rate in a maximum likelihood fashion. We tested this multimodal set-up on 14 human subjects and we report a maximum error of 0.5 BPM compared to the true breathing rate.

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A robust hybrid spectral estimation algorithm for SAR imaging

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284) ◽

10.1109/acssc.1998.751540 ◽

2002 ◽

Author(s):

Jian Li ◽

P. Stoica ◽

Zhaoqiang Bi ◽

Renbiao Wu ◽

E.G. Zelnio

Keyword(s):

Spectral Estimation ◽

Estimation Algorithm ◽

Sar Imaging

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Characterizing three‐dimensional wavefields with high‐resolution spectra

Geophysics ◽

10.1190/1.1443266 ◽

1992 ◽

Vol 57 (4) ◽

pp. 522-531

Author(s):

R. A. Meek ◽

A. A. Vassiliou

Keyword(s):

Fourier Transform ◽

Maximum Entropy ◽

Fourier Transforms ◽

Spectral Estimation ◽

Three Dimensional ◽

Estimation Method ◽

Synthetic Data ◽

Estimation Algorithm ◽

Three Dimensions ◽

Coherent Noise

Three‐dimensional spectra (frequency‐x‐wavenumber‐y‐wavenumber or [Formula: see text] spectra) can be used to determine the frequency content, velocity, and direction of waves entering an array of receivers. This information is important in detecting aliasing problems, understanding coherent noise, designing arrays, and determining parameters for coherent noise filters. Because of the limited spatial dimensions of most arrays the discrete Fourier transform produces an estimate of the three‐dimensional (3-D) spectrum with severe wavenumber distortion. We extend a 2-D hybrid spectral estimation method to three dimensions by combining a temporal Fourier transform with a spatial 2-D maximum entropy spectral estimation technique. The method produces [Formula: see text] spectra with higher wavenumber resolution and less spectral distortion than corresponding 3-D Fourier spectra. The 2-D maximum entropy spectral estimation algorithm uses a sequence of Fourier transforms to extrapolate the estimated autocorrelation function of the data. We assume the wavenumber spectrum of the data comprises a sum of a few poles. Field and synthetic data are used to demonstrate how 3-D wavefields can be characterized with this method of spectral analysis. From these results we conclude that the method gives excellent wavenumber resolution but performs poorly in detecting small signals in the presence of high amplitude signals. We feel this limitation is not serious for characterizing strong amplitude coherent energy recorded by an array of receivers.

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