Frequency-modulated continuous-wave LiDAR compressive depth-mapping

We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. % but can operate with only one at the cost of doubling the number of measurments. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. %Moreover, we show how a single total-variation minimization and two fast least-squares minimizations, instead of a single complex nonlinear minimization, can efficiently recover high-resolution depth-maps with minimal computational overhead. Moreover, by efficiently storing only $2m$ data points from $m<n$ measurements of an $n$ pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods

Interferometric synthetic aperture sonar system supported by satellite

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Sparsity-based autoencoders for denoising cluttered radar signatures

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%

Design and application of microstrip leaky wave antennas for radar sensing

textThis dissertation investigates the application of the frequency-scanned beam of a microstrip leaky wave antenna (LWA) to track humans in the two-dimensional (2-D) range-azimuth plane. The history, operating principles and frequency-scanned properties of a microstrip LWA are first reviewed. The basic concept of using a microstrip LWA to track humans is verified by designing, building and testing a broadband microstrip LWA, developing the necessary processing algorithm, and collecting data using a vector network analyzer. A number of topics are then investigated to further advance the concept. First, the idea of combining the frequency-scanned antenna with a short-pulse ultra-wideband (UWB) radar is developed to realize a portable, real-time system for human tracking. The radar concept and the components of the system are discussed in detail. Line-of-sight and through-wall measurements of a human subject are carried out to demonstrate the performance. Second, a new LWA structure is proposed to achieve a narrower azimuth beam, which requires both a small leaky-wave attenuation constant and a long aperture. The transverse resonance method (TRM) is applied to analyze the proposed structure and the results are verified with measurements of a built prototype. Third, a new signal processing technique, compressive sensing, is applied to further improve the resolution in both the azimuth and down range dimensions. The technique is tested with simulation and measurement data and is shown to produce sharper target responses in both the down range and azimuth dimensions. Lastly, the radar cross-section (RCS) of a microstrip LWA is studied. The antenna mode scattering and structural mode scattering are modeled separately. A ray picture is provided to explain the observed time-domain features using the group delay of the leaky wave.Electrical and Computer Engineerin

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

A Short-Range FMCW Radar-Based Approach for Multi-Target Human-Vehicle Detection

In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-The-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $alpha$-$eta$ filter, features extraction, and finally classification of targets by means of a $k$-nearest neighbor ( $k$-NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks)

Motion Compensation for Near-Range Synthetic Aperture Radar Applications

The work focuses on the analysis of influences of motion errors on near-range SAR applications and design of specific motion measuring and compensation algorithms. First, a novel metric to determine the optimum antenna beamwidth is proposed. Then, a comprehensive investigation of influences of motion errors on the SAR image is provided. On this ground, new algorithms for motion measuring and compensation using low cost inertial measurement units (IMU) are developed and successfully demonstrated

Lossy compression and real-time geovisualization for ultra-low bandwidth telemetry from untethered underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution September 2008Oceanographic applications of robotics are as varied as the undersea environment itself. As underwater robotics moves toward the study of dynamic processes with multiple vehicles, there is an increasing need to distill large volumes of data from underwater vehicles and deliver it quickly to human operators. While tethered robots are able to communicate data to surface observers instantly, communicating discoveries is more difficult for untethered vehicles. The ocean imposes severe limitations on wireless communications; light is quickly absorbed by seawater, and tradeoffs between frequency, bitrate and environmental effects result in data rates for acoustic modems that are routinely as low as tens of bits per second. These data rates usually limit telemetry to state and health information, to the exclusion of mission-specific science data. In this thesis, I present a system designed for communicating and presenting science telemetry from untethered underwater vehicles to surface observers. The system's goals are threefold: to aid human operators in understanding oceanographic processes, to enable human operators to play a role in adaptively responding to mission-specific data, and to accelerate mission planning from one vehicle dive to the next. The system uses standard lossy compression techniques to lower required data rates to those supported by commercially available acoustic modems (O(10)-O(100) bits per second). As part of the system, a method for compressing time-series science data based upon the Discrete Wavelet Transform (DWT) is explained, a number of low-bitrate image compression techniques are compared, and a novel user interface for reviewing transmitted telemetry is presented. Each component is motivated by science data from a variety of actual Autonomous Underwater Vehicle (AUV) missions performed in the last year.National Science Foundation Center for Subsurface Sensing and Imaging (CenSSIS ERC