7,051 research outputs found
Mitigation of Through-Wall Distortions of Frontal Radar Images using Denoising Autoencoders
Radar images of humans and other concealed objects are considerably distorted
by attenuation, refraction and multipath clutter in indoor through-wall
environments. While several methods have been proposed for removing target
independent static and dynamic clutter, there still remain considerable
challenges in mitigating target dependent clutter especially when the knowledge
of the exact propagation characteristics or analytical framework is
unavailable. In this work we focus on mitigating wall effects using a machine
learning based solution -- denoising autoencoders -- that does not require
prior information of the wall parameters or room geometry. Instead, the method
relies on the availability of a large volume of training radar images gathered
in through-wall conditions and the corresponding clean images captured in
line-of-sight conditions. During the training phase, the autoencoder learns how
to denoise the corrupted through-wall images in order to resemble the free
space images. We have validated the performance of the proposed solution for
both static and dynamic human subjects. The frontal radar images of static
targets are obtained by processing wideband planar array measurement data with
two-dimensional array and range processing. The frontal radar images of dynamic
targets are simulated using narrowband planar array data processed with
two-dimensional array and Doppler processing. In both simulation and
measurement processes, we incorporate considerable diversity in the target and
propagation conditions. Our experimental results, from both simulation and
measurement data, show that the denoised images are considerably more similar
to the free-space images when compared to the original through-wall images
Accurate angle-of-arrival measurement using particle swarm optimization
As one of the major methods for location positioning, angle-of-arrival (AOA) estimation is a significant technology in radar, sonar, radio astronomy, and mobile communications. AOA measurements can be exploited to locate mobile units, enhance communication efficiency and network capacity, and support location-aided routing, dynamic network management, and many location-based services. In this paper, we propose an algorithm for AOA estimation in colored noise fields and harsh application scenarios. By modeling the unknown noise covariance as a linear combination of known weighting matrices, a maximum likelihood (ML) criterion is established, and a particle swarm optimization (PSO) paradigm is designed to optimize the cost function. Simulation results demonstrate that the paired estimator PSO-ML significantly outperforms other popular techniques and produces superior AOA estimates
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