Radio-frequency (RF)-based high-resolution human imaging is an emerging area of research fueled by the increasing availability of RF-radar devices. Even though existing works achieve accurate human body reconstruction for pose estimation purposes, human identification with imaging has not been feasible due to its limited resolution. In this work, we present high-resolution neural network (HRNet), a deep neural network based on conditional generative adversarial network architecture, to achieve high-resolution human silhouette images, which can be used for human identification. HRNet uses radar spatial spectrum generated using a modified multiple signal classification algorithm as input and is trained with Kinect images as ground truth. We tested our design using a commodity millimeter-wave radar device operating at 60 GHz. Experiments performed with 12 users in three different environments show that our proposed system can reconstruct human images with 4% mean silhouette difference when compared with Kinect images. Moreover, the system achieved an average classification accuracy of 90.6% for 12 users and 95.0% for seven users in unseen environments; thereby proving robustness to environment changes.

In-car child presence detection (CPD) has gained worldwide attention due to increased child deaths reported yearly when they are left unattended in a car. Existing solutions usually require dedicated sensors and are being surpassed by WiFi-based CPD because the latter can provide broader coverage and can reuse the in-car WiFi devices. However, the existing WiFi-based CPD solutions are not robust and may suffer from miss detection due to the very weak breathing of a young child and high false alarms under unfavorable environmental conditions. In this paper, we propose a WiFi-based robust CPD system consisting of a motion and breathing detector. To improve breathing detection, we propose to treat the intermediate spectrogram for breathing estimation as images and apply image enhancement techniques followed by effective false alarm removal. Extensive experimental results have confirmed the robustness of the proposed system with a 99% detection accuracy and 3% false alarm rate.

Achieving accurate human identification through RF imaging has been a persistent challenge, primarily attributed to the limited aperture size and its consequent impact on imaging resolution. The existing imaging solution enables tasks such as pose estimation, activity recognition, and human tracking based on deep neural networks by estimating skeleton joints. In contrast to estimating joints, this paper proposes to improve imaging resolution by estimating the human figure as a whole using conditional generative adversarial networks (cGAN). In order to reduce training complexity, we use an estimated spatial spectrum using the MUltiple SIgnal Classification (MUSIC) algorithm as input to the cGAN. Our system generates environmentally independent, high-resolution images that can extract unique physical features useful for human identification. We use a simple convolution layers-based classification network to obtain the final identification result. From the experimental results, we show that resolution of the image produced by our trained generator is high enough to enable human identification. Our finding indicates high-resolution accuracy with 5% mean silhouette difference to the Kinect device. Extensive experiments in different environments on multiple testers demonstrate that our system can achieve 93% overall test accuracy in unseen environments for static human target identification.

We propose a weighted least-squares (WLS) design method for multi-dimensional (M-D) complex-coefficient finite-extent impulse response (FIR) filters. We consider the general form of M-D FIR filters having arbitrary frequency responses and low group delays. We formulate the proposed WLS design as a second-order cone programming problem. Design examples confirm that the proposed method provides the state-of-the-art M-D FIR filter designs with almost constant group delay.

Geometric information of scenes available with four-dimensional (4-D) light fields (LFs) paves the way for post-capture refocusing. Light field refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this letter, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 63% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.

A combined approach of low-complexity light field depth filtering and deep learning is proposed for object classification in the presence of partial occlusions. The proposed approach exploits depth information embedded in multi-perspective four-dimensional (4-D) light fields via low-complexity 4-D sparse depth filtering and deep-learning. The proposed 4-D depth filter, designed using numerical optimization techniques by formulating as an ℓ 1 - ℓ ∞ minimization problem, is shown to outperform typical light field refocusing based on 4-D shift-sum averaging filters. Experiments conducted using a light field dataset acquired by a Lytro camera verify 45% and 27% better performance in terms of object classification accuracy compared to the cases when no depth filtering is employed and standard shift-sum refocusing is employed, respectively.

A moving object in a five-dimensional (5-D) light field video (LFV) can be selectively enhanced using the depth and the velocity of the object. In this paper, a 5-D depth-velocity (DV) filter is proposed to enhance multiple moving objects at different depths and with different velocities in an LFV. The 5-D DV filter is designed as a cascade of an infinite-extent impulse response multi-depth filter and a finite-extent impulse response multi-velocity filter. Experimental results obtained with numerically generated and real LFVs indicate that more than 15 dB improvement in signal-to-interference ratio can be achieved with the proposed 5-D multi DV filter compared to previously proposed multi depth-only filters.

A minimax design for 2-D complex-coefficient FIR filters having asymmetric frequency responses is proposed in this paper. We consider the general form of 2-D FIR filters with low group delay and formulate the minimax design as a semidefinite programming problem. The 2-D linear-phase FIR filters with conjugate-symmetric coefficients are a special case of the proposed design. Example filter designs having near-equiripple magnitude responses are presented to verify the effectiveness of the proposed design method.