Luis Úbeda-Medina

3-D High-Resolution Imaging Radar at 300 GHz With Enhanced FoV

We have developed a 3-D high-resolution radar at 300 GHz with a cell resolution of 1×1.6×1.6 cm3 at a standoff distance of 8 m for security applications. The selection of an operating frequency of 300 GHz, a bandwidth of 27 GHz, and a field of view of 50×90 cm2 improves the through-clothes imaging of person-borne concealed objects. The radars antenna design allows single-pixel imaging at a frame rate of two frames per second. A reduction in cost and power consumption and improvements in image quality are additional requirements taken into account in the design of this radar system.

DDS-Based Signal-Generation Architecture Comparison for an Imaging Radar at 300 GHz

Submillimeter-wave imaging radars for standoff detection require broadband and fast-switch signal-generation architectures in order to achieve a high-range-resolution and near-video-rate imaging system. The signal generation has a strong influence on the radar image quality, primarily due to the transmitted phase noise. Two direct digital synthesis-based architectures for continuous-wave linear-frequency-modulated signal generation have been fabricated and tested for a high-range-resolution imaging radar at 300 GHz for standoff sensing of person-borne concealed threats. In order to select the signal-generation architecture that ensures the proper operation of the imaging radar with a cost-saving objective, three figures of merit have been used: 1) radar image quality; 2) power consumption; and 3) cost. The impact of the signal-generation architectures on the imaging radar indicates that both architectures present a similar radar performance in terms of radar image quality, although the narrowband direct-digital-synthesis/phase-locked loop scheme is a cost-effective solution compared with the broadband direct-digital-synthesis scheme in order to develop an affordable and energy-efficient high-performance preindustrial radar prototype.

Adaptive Auxiliary Particle Filter for Track-Before-Detect With Multiple Targets

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

Optimization of a compact THz imaging radar for real-time operation

A summary of obstacles and solutions in the Universidad Politécnica de Madrids CW-LFM (continuous-wave linear frequency modulation) imaging radar towards enabling real-time through-clothes threat detection with a compact system is presented. Image acquisition has been designed to permit a 2 fps (frames per second) video feed, and a newly developed signal-processing and image representation software, run on a mixed CPU-GPGPU (central processing unit general-purpose graphics processing unit) architecture, allows speeds up to 5 fps. Additional hardware modifications have yielded reductions in cost, power consumption, and volume.

Target tracking using multiple auxiliary particle filtering

Particle filters are a widely used tool to perform Bayesian filtering under nonlinear dynamic and measurement models or non-Gaussian distributions. However, the performance of particle filters plummets when dealing with high-dimensional state spaces. In this paper, we propose a method that makes use of multiple particle filtering to circumvent this difficulty. Multiple particle filters partition the state space and run an individual particle filter for every component. Each particle filter shares information with the rest of the filters to account for the influence of the complete state in the observations collected by sensors. The method considered in this paper uses auxiliary filtering within the MPF framework, outperforming previous algorithms in the literature. The performance of the considered algorithm is tested in a multiple target tracking scenario, with fixed and known number of targets, using a sensor network with a nonlinear measurement model.

Implementation of the fully adaptive radar framework: Practical limitations

The fully adaptive radar framework aims to take advantage of the available information about the environment in which the system is deployed to improve its performance, typically reducing the uncertainty of the results or optimizing the use of the available resources. In this paper we analyze the performance of this approach when applied to the problem of target tracking with nonlinear range-dependent measurement models in a scenario with resource constraints. Our purpose is to characterize the limitations that arise in these type of scenarios, which should be taken into account when considering the implementation of the fully adaptive radar approach.

Simulation Framework for a 3-D High-Resolution Imaging Radar at 300 GHz with a Scattering Model Based on Rendering Techniques

We present a simulation framework for a 3-D high-resolution imaging radar at 300 GHz with mechanical scanning. This tool allows us to reproduce the imaging capabilities of the radar in different setups and with different targets. The simulations are based on a ray-tracing approximation combined with a bidirectional reflectance distribution function (BRDF) model for the scattering of rough surfaces. Moreover, we present a novel approach to estimate the scattering parameters of the BRDF model for different types of targets from the combination of the radar data and information obtained from an infrared structure light sensor. This new framework will serve as a baseline for the design of future radar multistatic configurations and to generate synthetic data to train automatic target recognition algorithms.

Multiple target tracking in the fully adaptive radar framework

The fully adaptive radar framework aims to use some known information, or cognition, about the environment in which the system is deployed to obtain some improvement in its performance, typically either reducing the uncertainty of the obtained results or optimizing the use of the available resources. In this paper, the extension of the fully adaptive radar framework for the case of multiple target tracking is introduced. In order to illustrate the proposed framework use, a simulation is carried out in an scenario comprising multiple targets and a sensor network with resource constraints. Results show a remarkable performance improvement when the proposed fully adaptive radar approach is used.

Characterization of a 300 GHz imaging radar for standoff detection

A complete characterization of a 300 GHz high-resolution imaging radar with large Field of View is carried out in order to optimize its operational parameters. This imaging radar has been designed for standoff detection in security applications. The characterization is focused primarily on the spatial and range resolution. The results show that the imaging radar presents a resolution better than 2 cm. 3D images revealing a threat hidden under clothing validate the imaging radar performance.