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Radar target simulators are not only a critical tool for verifying and testing radar systems but also play an important role in supporting the development of self-driving cars. Advances in radar sensors and techniques raise the required specifications for these units, increasing their complexity and cost. This article presents a novel and universal concept for radar target simulators that addresses these issues by responding only to the transmitted signal of a radar sensor during a fraction of the time, therefore modulating the average of the signal. This offers advantages for three independent use cases, which may be combined. First, the dynamic range and resolution of simulated target echo power can be improved even for existing systems. Second, the simulation of multiangle scenarios with a single backend is possible with this approach. Finally, hardware complexity and power consumption can be reduced. The proposed concept is examined extensively for frequency-modulated continuous wave radar, and design decisions are made. The theoretical considerations are validated with measurements with a real radar target simulator showing an improvement of up to $30 \,\mathrm{dB}$ in the dynamic range with no observable negative side effects.

Modern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environment perception in complex dynamic situations. Among lidars and cameras, radars are typical for these applications, but they generate huge amounts of data, which cannot be transmitted or stored effectively in current setups. Consequently, manufacturers usually have to process the data “on sensor.” This results in transmitting only a few extracted features as point clouds or object lists to a central processing unit, which usually causes a significant loss of information. With this approach, advanced processing—such as enhancement of resolution by coherent combination of sensors or ghost target removal with advanced algorithms—is hardly possible. To overcome this, we suggest an alternative method by using signal-based compression with defined losses. The following topology will be proposed: the sensors encode raw data without prior radar-specific processing and after transmission, a central unit decodes and processes the radar data, thus benefiting from its more powerful heterogeneous processing system. We will analyze lossless compression algorithms with rate savings of about 30% to 65%, but the focus is on lossy compression algorithms that incorporate higher compression ratios by allowing negligible errors. It is shown that state-of-the-art multimedia compression algorithms can obtain rate savings of 99%, and radar specific algorithms can add a 50-fold gain on top, reaching 99.98%. To assess the distortions of compressed data, we then present different radar-specific evaluation metrics.

Recently, a novel chipless radio-frequency identification (RFID) concept based on polarimetric radar barcodes was introduced. These tags are similar to the well known optical quick response codes (QR codes). In polarimetric radar barcodes the information is stored as polarimetrically coded areas arranged in a grid and read out by an imaging radar. This paper presents a novel concept for elegantly creating polarimetric barcodes based on dipole scattering domains. Each scattering domain consists of several hundred dipoles. The dipoles are either uniformly arranged at a specific angle, which produces a specific angle-dependent polarization response, or randomly oriented to produce an unpolarized response. In order to read out the polarimetric information stored in the tags, the position and rotation of the radar barcodes needs to be determined. Our research reveals that using three reference elements and a novel decoding algorithm, a position- and rotation-tolerant readout is possible. To reduce distortions caused by the surface of the structure carrying the dipoles, different matching layer concepts were investigated. The novel chipless RFID tag concept is tested using a fully polarimetric W-band (75-$110 \,\mathrm{G}\mathrm{Hz}$) imaging radar. The implemented setup allows for a theoretical data capacity of $1.92 \,\mathrm{bit} \mathrm{/}\mathrm{c}\mathrm{m}^{2}\,$ on a single layer PCB. In our measurements, we achieved a data capacity of $1.52 \,\,\mathrm{bit} \mathrm{/}\mathrm{c}\mathrm{m}^{2}\,$. Utilizing the chosen tag size of $45 \,\mathrm{m}\mathrm{m} \times 45 \,\mathrm{m}\mathrm{m}$ resulted in a tag with a greater than 30 bit data capacity, which is an excellent value for chipless RFID.

In this paper, we introduce an ultra-high frequency radio frequency identification (UHF-RFID) mobile robot platform that is capable of performing fully autonomous inventory taking and stocktaking by providing three-dimensional (3D) product maps and thus making possible the concept of a smart warehouse. The proposed novel hardware architecture consists of an eight-channel UHF-RFID-listener for parallel signal phase recovery, including two different carrier leakage suppression circuits and a correlation decoder for each channel for the tag signal, which can handle a backscatter link frequency (BLF) deviation of up to 22 % to decode the tag data. The system also uses eight parallel channels for multiple-input multiple-output (MIMO) localization. For the system evaluation we labeled clothes stored in a warehouse with tags and generated their product map. The proposed localization algorithm is based on a synthetic aperture radar (SAR) MIMO approach that needs exact knowledge of the antenna positions and, therefore, of the driven trajectory. This position is provided by the robot, which takes advantage of a simultaneous localization and mapping (SLAM) algorithm, determining the position with 1 cm accuracy while generating two-dimensional (2D) maps of the surroundings. We placed ten tags at known positions to assess the system's performance and were able to locate these tags within a root mean square error (RMSE) of 1.45 cm in 3D.