PubMed · 719312
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B King, C A Marsden. 1978. Monitoring motor activity using Doppler shift radar [proceedings].. https://pubmed.ncbi.nlm.nih.gov/719312/
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The performance of small-diameter targets suitable for use as oscillating targets for testing the sensitivity of Doppler fetal heartbeat detectors has been systematically studied. Experimental results are presented in the 1.6-3.0 MHz frequency range for the plane-wave reflection loss for a total of 16 targets: spherical balls made of stainless steel; hemispherical-ended rods made of PTFE; and flat-ended rods made of stainless steel, PTFE, polycarbonate, and tungsten carbide. Results show that the fine-structure variation of reflection loss with frequency is greatest in the case of spherical ball targets and least for flat-ended targets. It has been shown that, providing care is taken during manufacture, the reflection loss from a flat-ended target can be predicted using a simple theory based on a plane disc reflector. Tungsten carbide targets consisting of a long rod with a diameter of 1.6 mm tapered down to a cylindrical flat end with a diameter of 0.4, 0.5, and 0.6 mm have been shown to provide reflection losses of between 60 and 40 dB, and to have a smooth variation of reflection loss with frequency. They can also be manufactured in a form that allows no significant interference from the supporting structure and, therefore, are ideal targets to meet the requirements of International Electrotechnical Commission 1266:1995.
This paper presents a novel approach to the segmentation and integration of (radar) images using a second-order recurrent artificial neural network architecture consisting of two sub-networks: a function network that classifies radar measurements into four different categories of objects in sea environments (water, oil spills, land and boats), and a context network that dynamically computes the function network's input weights. It is shown that in experiments (using simulated radar images) this mechanism outperforms conventional artificial neural networks since it allows the network to learn to solve the task through a dynamic adaptation of its classification function based on its internal state closely reflecting the current context.
The performance of three spectral techniques (FFT, AR Burg and ARMA) for maximum frequency estimation of the Doppler spectra is described. Different definitions of fmax were used: frequency at which spectral power decreases down to 0.1 of its maximum value, modified threshold crossing method (MTCM) and novel geometrical method. "Goodness" and efficiency of estimators were determined by calculating the bias and the standard deviation of the estimated maximum frequency of the simulated Doppler spectra with known statistics. The power of analysed signals was assumed to have the exponential distribution function. The SNR ratios were changed over the range from 0 to 20 dB. Different spectrum envelopes were generated. A Gaussian envelope approximated narrow band spectral processes (P. W. Doppler) and rectangular spectra were used to simulate a parabolic flow insonified with C. W. Doppler. The simulated signals were generated out of 3072-point records with sampling frequency of 20 kHz. The AR and ARMA models order selections were done independently according to Akaike Information Criterion (AIC) and Singular Value Decomposition (SVD). It was found that the ARMA model, computed according to SVD criterion, had the best overall performance and produced results with the smallest bias and standard deviation. In general AR(SVD) was better than AR(AIC). The geometrical method of fmax estimation was found to be more accurate than other tested methods, especially for narrow band signals.