A Convolutional Neural Network Approach to Parking Monitoring in Urban Radar Sensing - Martinez Garcia, Javier; Zoeke, Dominik; Vossiek, Martin (European Microwave Week / EuRAD, 2017)

A Modular MIMO Millimeter-Wave Imaging Radar System for Space Applications and Its Components - Hrobak, Michael; Thurn, Karsten; Moll, Jochen; Hossain, Maruf; Shrestha, Amit; Thualfiqar, Al-Sawaf; Stoppel, Dimitri; Weimann, Nils G.; Rämer, Adam; Heinrich, Wolfgang; Martinez, Javier; Vossiek, Martin; et al. (Journal of Infrared, Millimeter, and Terahertz Waves, 2020)
This article presents the design and prototyping of components for a modular multiple-input-multiple-output (MIMO) millimeter-wave radar for space applications. A single radar panel consists of 8 transmitters (TX) and 8 receivers (RX), which can be placed several times on the satellite to realize application-specific radar apertures and hence different cross-range resolutions. The radar chirp signals are generated by SiGe:C BiCMOS direct-digital-synthesizers (DDS) in the frequency range of 1 to 10.5GHz with a chirp repetition rate of <1μs within each TX and RX. The latter allows for easy interfaces in the MHz range in between the TX/RX units and therefore optimized 2-D sparse antenna arrays with rather large distances in between the TX/RX antennas. Furthermore, this allows for ideally linear frequency modulated continuous-waveforms (FMCW) in conjunction with phase-shift-keying (PSK) radar signals and enables simultaneous operation of all TX when code division multiplex (CDMA) modulation schemes are applied. Comparably low complexity of the TX/RX units has been achieved by applying straightforward frequency plans to signal generation and detection but comes with challenging requirements for the individual active and passive components. Tackled by thin film technology on alumina and the recently developed SiGe and InP semiconductor technologies, which have been further optimized in terms of process maturity and space qualification. Upconversion and downconversion to and from 85 to 94.5GHz are performed by double balanced Gilbert mixers realized with InP double heterojunction bipolar transistor technology (DHBT) and 42-GHz local oscillator signals from SiGe:C BiCMOS VCO synthesizer using phase-locked-loops (PLL). InP DHBT power amplifiers and low-noise amplifiers allow for output power levels of 15dBm and >30dB gain with noise figure values of 9dB, respectively. The MIMO radar utilizes patch antenna arrays on organic multilayer printed circuit boards (PCB) with 18dBi gain and 18∘ half power beamwidth (HPBW). Generation of power supply and control signals, analog-to-digital conversion (ADC), and radar signal processing are provided centrally to each panel. The radar supports detection and tracking of satellites in distances up to 1000m and image generation up to 20m, which is required to support orbital maneuvers like satellite rendezvous and docking for non-cooperative satellites.

Broadband beamforming and null-steering based on fractional delay in OFDM systems - Martinez Garcia, Javier; Zarzuelo, C; Iglesias, V; Grajal, J (20th European Wireless Conference, 2014)
lhft_intern.bib::Martinez14

Characterization of Rotating Objects with Tomographic Reconstruction of Multi-Aspect Scattered Signals - Martinez Garcia, Javier; Thurn, Karsten; Vossiek, Martin (IEEE Transactions on Aerospace and Electronic Systems, 2019)
Abstract—The backscattered signals of objects underspinning motion or with rotating parts provide very richinformation that can be used for classification tasks,parameter extraction etc. Obtaining such informationfrom non-cooperative objects with an unknown targetaspect is often a complicated task with a monostaticconfiguration. A multistatic radar on the other hand, canexploit the spatial diversity to extract the informationfrom the time-frequency representations obtained frommultiple aspect-angles. In this work, we propose a tomo-graphic approach for characterizing spinning objects interms of their shape, size and rotation parameters usinga narrowband multistatic radar. A 2D image is recon-structed after a full rotation period using tomographicmethods that allows not only to estimate the shape ofthe target, but also the rotation parameters and thedimensions of the object. This is done very efficientlyby combining the tomographic images from differentaspect-angles on the transformed log-polar space, insteadof the time-frequency representations. Simulations andmeasurements were conducted for the proof of concept.The measurement results with a simple target and a CWK-Band radar show errors below 3◦for the orientationestimation and below 5% for the estimation of the object'sdiamete
Keywords: Tomography; Image reconstruction; Radar imaging; Doppler shift; Shape; Narrowband; Multistatic radar; narrowband radar; tomographic reconstruction; total rotation velocity estimation

Convolutional Neural Network Assisted Detection and Localization of UAVs with a Narrowband Multi-site Radar - Martinez, Javier; Kopyto, David; Schütz, Martin; Vossiek, Martin (IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2018), 2018)
We present an approach to detect and locate non-cooperative UAVs from their micro-Doppler signature using a narrowband radar in a multi-site configuration. We describe a method for the localization of rotating objects with the geometric information obtained exclusively from their micro-Doppler signatures. This approach only requires very simple transceivers with CW waveforms, in a cost-effective multi-site architecture. A convolutional neural network is used to detect and identify the UAVs by extracting the characteristic features of their micro-Doppler signature. We present simulated and preliminary experimental data that show the technical viability of this concep
lhft_intern.bib::Martinez18a

Convolutional neural networks for parking space detection in downfire urban radar - Martinez Garcia, Javier; Zoeke, Dominik; Vossiek, Martin (International Journal of Microwave and Wireless Technologies, 2018)
We present a method for detecting parking spaces in radar images based on convolutional neural networks (CNN). A multiple-input multiple-output radar is used to render a slant-range image of the parking scenario and a background estimation technique is applied to reduce the impact of dynamic interference from the surroundings by separating the static background from moving objects in the scene. A CNN architecture, that also incorporates mechanisms to generalize the model to new scenarios, is proposed to determine the occupancy of the parking spaces in the static radar images. The experimental results show very high accuracy even in scenarios where little or no training data is available, proving the viability of the proposed approach for its implementation at large scale with reduced deployment effort
lhft_intern.bib::Martinez18c

Deep Learning-Based Segmentation for the Extraction of Micro-Doppler Signatures - Martinez Garcia, Javier; Vossiek, Martin (Proceedings of the European Microwave Week (EuRAD), 2018)
We present a method for extracting micro-Doppler signatures using a deep convolutional neural network that learns to identify and separate relevant micro-Doppler components from the background. A modified convolutional neural network (fully convolutional network) is trained end-to-end to perform dense predictions from the micro-Doppler signature at the input, generating a map with labels on a pixel level at the output. The network learns intermediate representations with the characteristic patterns of the micro-Doppler paths generated by individual scatterers and is capable of identifying and locating them in the time-frequency representation. The model trained on a simulated environment shows very good performance metrics even in noisy environments, and the experimental results with a continuous wave (CW) radar at 24 GHz indicates that the model can be applied to real scenarios. Moreover, the method scales properly to more complex signatures when several components are superimposed in the time-frequency representation, which indicates that this concept might represent a promising approach for interpreting complex micro-Doppler signature
lhft_intern.bib::Martinez18b

Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning - Martinez Garcia, Javier; Prophet, Robert; Fuentes, Michel Juan Carlos; Ebelt, Randolf; Vossiek, Martin; Weber, Ingo (IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM 2019), 2019)
We introduce a method to classify ghost moving detections in automotive radar sensors for advanced driver assistance systems. A fully connected network is used to distinguish between real and false moving detections in the occupancy gridmaps. By using this architecture, we combine the local Doppler information, along with the spatial context of the surrounding scenario to classify the moving detections. A proof of concept experiment shows promising results with data from a test drive in an urban scenari

Instantaneous Ghost Detection Identification in Automotive Scenarios - Prophet, Robert; Martinez Garcia, Javier; Fuentes, Michel Juan Carlos; Ebelt, Randolf; Vossiek, Martin (Radar Conference 2019, Boston (MA), USA, April 2019, 2019)
This paper addresses one of the most serious drawbacks of automotive radar sensors: The frequent occurrence of ghost detections. As such detections may cause undesired behavior, they need to be partially or completely eliminated. We present an algorithm, which uses a machine-learning-based classifier to distinguish between real and ghost detections. In contrast to other papers, this approach addresses all causes of ghost detections and not only simple multipath scenarios. Real world experiments – also in challenging situations – show success rates of 91

MIMO-FMCW Radar-Based Parking Monitoring Application With a Modified Convolutional Neural Network With Spatial Priors - Martinez Garcia, Javier; Zoeke, Dominik; Vossiek, Martin (IEEE Access, 2018)
Radar imaging is a competitive option for smart city applications over optical approaches, as it raises no privacy concerns. The inherent difficulty of interpreting radar signals can be overcome using deep learning techniques to leverage the capabilities of monitoring sensors with a minimum of human intervention. In this paper, we use a modified convolutional neural network (CNN) for classifying radar images in order to detect vacant parking spaces with a 77-GHz imaging radar. Although training CNNs for radar-image classification is challenging due to poor generalization performance caused by the lack of labeled training data, the modified architecture takes into account the properties of the radar image in order to introduce prior information into the model and improve performance. A MIMO-FMCW radar is utilized to render a slant-range image of a parking scenario, and the image patches corresponding to each parking location are classified independently in the CNN. Since the radiation pattern of a MIMO array varies as a function of the scanning angle, the corresponding spatial coordinate of each patch is included as an additional feature in the upper layers of the network. This allows the model to combine local features from each patch with global scenario information in order to learn robust features that generalize properly to new scenarios. Several models are trained end to end with data from four different parking scenarios and evaluated in a 4-fold cross-validation scheme, and performance is improved when spatial prior information is include
lhft_intern.bib::Martinez18d

MIMO Radar for Supporting Automated Rendezvous Maneuvers with Non-Cooperative Satellites - Martinez Garcia, Javier; Thurn, Karsten; Vossiek, Martin (IEEE Radar Conference, 2017)

Performance evaluation of a multi-layer reconfigurable optical-wireless network architecture employing digitized RoF - Martinez Garcia, Javier; Tsekrekos, C; Tomkos, I; Huiszoon, B (Future Network and Mobile Summit, 2011)
lhft_intern.bib::Martinez11