Utilizing the complementary strengths of wavelength-specific range or depth sensors is crucial for robust computer-assisted tasks such as autonomous driving. Despite this, there is still little research done at the intersection of optical depth sensors and radars operating close range, where the target is decimeters away from the sensors. Together with a growing interest in high-resolution imaging radars operating in the near field, the question arises how these sensors behave in comparison to their traditional optical counterparts. In this work, we take on the unique challenge of jointly characterizing depth imagers from both, the optical and radio-frequency domain using a multimodal spatial calibration. We collect data from four depth imagers, with three optical sensors of varying operation principle and an imaging radar. We provide a comprehensive evaluation of their depth measurements with respect to distinct object materials, geometries, and object-to-sensor distances. Specifically, we reveal scattering effects of partially transmissive materials and investigate the response of radio-frequency signals. All object measurements are made public in form of a multimodal dataset, called MAROON, which can be accessed at: https://vwirth.github.io/maroon .

Large intelligent reflecting surfaces (IRSs) possess a large near-field (NF) range, which makes their configuration challenging. To reduce the potentially significant overhead associated with configuring these large IRSs, phase shift codebooks are a promising approach. However, most existing IRS codebooks focus on maximizing the beamforming gain of the IRS, rather than optimizing the signal-to-noise ratio (SNR) at the receiver, which is more critical to the quality of service (QoS). In this paper, we introduce an IRS codebook design that maximizes the minimum SNR within a target receiver volume by optimizing the IRS phase shifts. We observe that, in the considered NF range, maximizing the IRS reflection gain for the farthest surface of the target receiving volume is equivalent to maximizing the SNR within the entire volume. Based on this observation, we derive an analytical phase shift design, where each unit cell (UC) of the IRS focuses on a specific point. Next, we formulate a nonconvex optimization problem, for a second, improved codebook design, and find a local optimum for the minimum SNR based on sucessive convex approximation (SCA). The optimized design reveals a trade-off between the minimum SNR and the codebook size, while achieving excellent performance across the entire considered NF. Numerical evaluations show that both proposed designs outperform several baseline schemes from the literature.

In this paper, we study the optimization of the sensing accuracy of unmanned aerial vehicle (UAV)-based dual-baseline interferometric synthetic aperture radar (InSAR) systems. A swarm of three UAV-synthetic aperture radar (SAR) systems is deployed to image an area of interest from different angles, enabling the creation of two independent digital elevation models (DEMs). To reduce the InSAR sensing error, i.e., the height estimation error, the two DEMs are fused based on weighted averaging techniques into one final DEM. The heavy computations required for this process are performed on the ground. To this end, the radar data is offloaded in real time via a frequency division multiple access (FDMA) air-to-ground backhaul link. In this work, we focus on improving the sensing accuracy by minimizing the worst-case height estimation error of the final DEM. To this end, the UAV formation and the power allocated for offloading are jointly optimized based on alternating optimization (AO), while meeting practical InSAR sensing and communication constraints. Our simulation results demonstrate that the proposed solution can significantly improve the sensing accuracy compared to classical single-baseline UAV-InSAR systems and other benchmark schemes.

In this paper, we investigate joint unmanned aerial vehicle (UAV) formation and resource allocation optimization for communication-assisted three-dimensional (3D) synthetic aperture radar (SAR) sensing. We consider a system consisting of two UAVs that perform bistatic interferometric SAR (InSAR) sensing for generation of a digital elevation model (DEM) and transmit the radar raw data to a ground station (GS) in real time. To account for practical 3D sensing requirements, we use non-conventional sensing performance metrics, such as the interferometric coherence, i.e., the local cross-correlation between the two co-registered UAV SAR images, the point-to-point InSAR relative height error, and the height of ambiguity, which together characterize the accuracy with which the InSAR system can determine the height of ground targets. Our objective is to jointly optimize the UAV formation, speed, and communication power allocation for maximization of the InSAR coverage while satisfying energy, communication, and InSAR-specific sensing constraints. To solve the formulated non-smooth and non-convex optimization problem, we divide it into three sub-problems and propose a novel alternating optimization (AO) framework that is based on classical, monotonic, and stochastic optimization techniques. The effectiveness of the proposed algorithm is validated through extensive simulations and compared to several benchmark schemes. Furthermore, our simulation results highlight the impact of the UAV-GS communication link on the flying formation and sensing performance and show that the DEM of a large area of interest can be mapped and offloaded to ground successfully, while the ground topography can be estimated with centimeter-scale precision. Lastly, we demonstrate that a low UAV velocity is preferable for InSAR applications as it leads to better sensing accuracy.

Interferometric synthetic aperture radar (InSAR) is an increasingly important remote sensing technique that enables three-dimensional (3D) sensing applications such as the generation of accurate digital elevation models (DEMs). In this paper, we investigate the joint formation and communication resource allocation optimization for a system comprising two unmanned aerial vehicles (UAVs) to perform InSAR sensing and to transfer the acquired data to the ground. To this end, we adopt as sensing performance metrics the interferometric coherence, i.e., the local correlation between the two co-registered UAV radar images, and the height of ambiguity (HoA), which together are a measure for the accuracy with which the InSAR system can estimate the height of ground objects. In addition, an analytical expression for the coverage of the considered InSAR sensing system is derived. Our objective is to maximize the InSAR coverage while satisfying all relevant InSAR-specific sensing and communication performance metrics. To tackle the non-convexity of the formu-lated optimization problem, we employ alternating optimization (AO) techniques combined with successive convex approximation (SCA). Our simulation results reveal that the resulting resource allocation algorithm outperforms two benchmark schemes in terms of InSAR coverage, while satisfying all sensing and real-time communication requirements. Furthermore, we highlight the importance of efficient communication resource allocation in facilitating real-time sensing and unveil the trade-off between InSAR height estimation accuracy and coverage.

Integrating different functionalities, conventionally implemented as dedicated systems, into a single platform allows utilising the available resources more efficiently. We consider an integrated sensing and power transfer (ISAPT) system and propose the joint optimisation of the rectangular pulse-shaped transmit signal and the beamforming vector to combine sensing and wireless power transfer (WPT) functionalities efficiently. In contrast to prior works, we adopt an accurate non-linear circuit-based energy harvesting (EH) model. We formulate and solve a non-convex optimisation problem for a general number of EH receivers to maximise a weighted sum of the average harvested powers at the EH receivers while ensuring the received echo signal reflected by a sensing target (ST) has sufficient power for estimating the range to the ST with a prescribed accuracy within the considered coverage region. The average harvested power is shown to monotonically increase with the pulse duration when the average transmit power budget is sufficiently large. We discuss the trade-off between sensing performance and power transfer for the considered ISAPT system. The proposed approach significantly outperforms a heuristic baseline scheme based on a linear EH model, which linearly combines energy beamforming with the beamsteering vector in the direction to the ST as its transmit strategy.

Despite an emerging interest in MIMO radar, the utilization of its complementary strengths in combination with optical depth sensors has so far been limited to far-field applications, due to the challenges that arise from mutual sensor calibration in the near field. In fact, most related approaches in the autonomous industry propose target-based calibration methods using corner reflectors that have proven to be unsuitable for the near field. In contrast, we propose a novel, joint calibration approach for optical RGB-D sensors and MIMO radars that is designed to operate in the radar’s near-field range, within decimeters from the sensors. Our pipeline consists of a bespoke calibration target, allowing for automatic target detection and localization, followed by the spatial calibration of the two sensor coordinate systems through target registration. We validate our approach using two different depth sensing technologies from the optical domain. The experiments show the efficiency and accuracy of our calibration for various target displacements, as well as its robustness of our localization in terms of signal ambiguities.

The polar regions of Mars as well as the ice-covered moons such as Saturn’s Enceladus and Jupiter’s Europa have emerged as significant targets for ongoing and future space missions focused on investigating potentially habitable celestial bodies within our solar system. A key objective of these missions is to explore subglacial water reservoirs lying beneath the ice crusts of moons, such as Europa. The utilization of melting probes shows immense promise for achieving this goal. However, in addition to the capability to melt through the ice body, such a probe must also be able to identify the ice-water interface as well as obstacles in its path, such as cavities or meteoric rocks. To address these challenges, we present a forefield reconnaissance system (FRS) featuring a hybrid sensing approach that combines radar and sonar both integrated into the tip of a melting probe. Furthermore, the system includes an in-situ permittivity sensor to ensure accurate radar range assignment and to gather scientific data about the ice body. The system has been integrated into a demonstrator melting probe and tested in a terrestrial analogue scenario. Measurements at the Jungfraufirn in Switzerland confirm the potential of the developed system.

To meet the ever-increasing requirements of high-rate data transmission, the significant amount of spectrum available in the TeraHertz (THz) band is considered for future wireless communications. However, the performance of THz communications is limited by strong phase noise (PN) introduced by oscillators and the complexity added by channel state information (CSI) acquisition. To overcome such impediments, we propose a new transmission concept based on constant-weight (CW) codes which enable low-complexity maximum-likelihood (ML) sequence detection at the receiver side in the presence of strong PN without requiring statistical or instantaneous CSI knowledge. In addition, the error rate of the ML receiver for the proposed CW codes is analyzed. Simulation results verify the analytical derivations and illustrate that the proposed transmission scheme outperforms transmission with on-off keying modulation and coherent detection.

This paper presents a wideband 12 Bit 8 GS/s time-interleaved successive approximation register (SAR) analog-to-digital converter (ADC), featuring a sub-2 radix architecture with an overrange of 10% and randomized sampling with mismatch correction in a 28 nm CMOS technology. For this purpose, 18 500 MHz SAR-ADCs plus an additional reference ADC are interleaved. This topology enables a randomization approach, reducing mismatch related interleaving spurs. Furthermore, the additional reference ADC operating in parallel to the main ADC enables adaptive digital calibration to correct for static and time-interleaved mismatch effects. A wideband front-end features two subsequent push-pull buffer stages to achieve a high track- and-hold (T/H) bandwidth and high sampling linearity, while improving kickback related settling limitations. After calibration, the ADC achieves a signal to noise and distortion ratio (SNDR) of 56.8 dB and a spurious free dynamic range (SFDR) of 80 dBc applying a single full scale sine wave tone close to the Nyquist frequency of 4 GHz.

As radar sensors can measure an object's range and velocity with a high degree of precision, moving objects can be successfully classified, as well. Classifying stationary objects still needs a lot of research, however. In this paper, we use popular semantic segmentation networks in order to classify the vehicle's immediate infrastructure. To this end, a full 3D measurement is performed with a test vehicle equipped with four high resolution corner radar sensors. A preprocessed point cloud is transformed into various radar maps for input to a neural network. Simulations as well as real-world measurements show an overall intersection over union of 84 and 77%, respectively, as well as an overall accuracy of 95 and 90%, respectively, being a new benchmark for this young research field.

Radar sensors have become an integral part of advanced driver assistance systems. Merely detecting targets will not, however, advance their contribution. Rather, an object classification capability is required to distinguish vulnerable road users from other objects, such as vehicles. To achieve this, targets that are determined from the range-Doppler-Matrix created by a 79 GHz chirp sequence radar are clustered to objects. Different classifiers then use previously calculated characteristic features of moving objects to generate the object classes “Pedestrian” and “Other”. As a result, the success rate of one measurement reaches up to 95.3% for well-suited classifiers and a bandwidth of 1.6 GHz. Moreover, the robustness of the classification process is increased by tracking the objects. The proposed algorithm for pedestrian classification is not only faster than conventional approaches using micro-Doppler signatures, but also requires less computational effort. Implemented in vehicles, this can be a major contribution to protect vulnerable road users such as pedestrians.

In this paper, we propose a robust minimum maximum mean square error Tomlinson-Harashima precoding (Min-Max-MSE THP) scheme and a low-complexity robust Min-Max-MSE hierarchical linear/THP (HL-THP) scheme for downlink massive multiuser multiple-input-multiple-output (MU-MIMO) systems with imperfect channel state information (CSI) at the transmitter. The proposed robust Min-Max-MSE HL-THP scheme comprises an inner linear beamformer (BF), which is designed based on second-order CSI statistics, and outer THP modules, which exploit the instantaneous overall CSI of the cascade of the actual channel and the inner BF. Thereby, the user terminals are divided into groups, where for each group a THP module successively mitigates the intra-group interference, whereas the inter-group interference is canceled by the inner BF. To ensure fairness, we adopt the maximization of the asymptotic signal-to-leakage-plus-noise ratio in the large system limit and the Min-Max-MSE as an optimization criterion for designing the inner BF and the per-group THP modules, respectively. Our analytical and simulation results show that the proposed robust Min-Max-MSE HL-THP scheme achieves a substantially improved performance in terms of the Max-MSE, maximum bit error rate, and minimum rate compared to linear regularized zero-forcing precoding. Moreover, the performance loss of the proposed robust Min-Max-MSE HL-THP scheme compared to the robust Min-Max-MSE THP scheme is small. In addition, our complexity analysis reveals that the proposed robust Min-Max-MSE HL-THP scheme has a much lower computational complexity than the Min-Max-MSE THP scheme. Hence, the robust Min-Max-MSE HL-THP scheme provides a favorable tradeoff between complexity and performance.

The implementation of lightweight robot concepts requires novel measurement and control strategies to deal with the flexibility of the robot arm. To the authors' best knowledge this paper introduces for the first time an innovative closed-loop concept to measure and control the absolute position of the tool center point (TCP) based on a synthetic aperture wireless localization approach. A radio-frequency identification (RFID)-like backscatter transponder is attached to the TCP, and several radar base stations measure the respective roundtrip time-of-flight and phase of the backscattered transponder signals. Inverse radar apertures are synthesized based on a small movement of the TCP. The small TCP trajectory is tracked with simple relative sensors. A holographic synthetic aperture reconstruction algorithm then determines the absolute TCP position - i.e., the trajectory coordinates in the world coordinate system. This absolute positional information is used to observe the control system and identify model errors, which are minimized in a closed-loop procedure. This iteratively improves both the system model and control quality as well as the precision of the synthetic aperture wireless localization. System simulations and first test results prove that the novel concept is suited to measure and control the position of an end-effector with mm-precision, even if the initial system model is partly erroneous.

Reading ranges are being extended in the wake of recent advances in UHF radio frequency identification (RFID) systems, and with the advent of larger reading ranges, tag localization has moved into the spotlight. Recently, we introduced a new UHF RFID tag localization technique. The proposed method is based on phase measurements taken along a synthetic aperture. A holographic image is calculated based on the scanned phase values. The image represents the spatial probability density function for the actual tag location. This paper presents this innovative method in detail. Simulations that illustrate the effect of the given trajectory are included. Extensive measurements obtained in a reflective lab environment are presented. We discuss the method's effectiveness with respect to measurement errors, antenna phase center distortions, and the available phase information. The results show the potential practical applications for the method in moving reader antennas, such as handheld readers or readers mounted on vehicles like forklifts or mobile robotic systems.

The accuracy of an FMCW radar working in a multipath environment is mainly limited by the radar signal bandwidth. Furthermore, the separation and identification of line-of-sight (LOS) and non-line-of-sight (NLOS) signals are important and challenging problems in many radar and local positioning applications. In this article, a method is proposed to overcome these restrictions. A state observer is used for an improved fusion of the FMCW radar beat signal frequency and phase. It is shown that the concepts allow for an accuracy improvement of more than one order of magnitude. In addition, the new approach is able to distinguish between NLOS and LOS measurements. The robustness of the system is improved by the implementation of motion type detection algorithms. Practical experiments illustrate that the new technique is superior to standard FMCW radar location methods.

Many applications for FMCW radar systems require the resolution of several closely spaced frequencies. In general the Fast Fourier Transform (FFT) is used for spectrum estimation, despite the inherent resolution problems. To overcome the resolution limitation the state space approach has been proposed by Rao and Arun (1992), Marple (1988) and Kay (1988). However, most experimental results in the above papers are based on simulations. This paper points out the differences of the "real world" signal structure to simulated signals, and describes the problems posed by real radar signals. For one of the key steps of the state space algorithm, model order selection, a novel algorithm based on a posteriori analysis is introduced. The feasibility of the new approach is verified with an actual 24 GHz level gauging FMCW radar system. Our approach yields stable and accurate results with a resolution approximately three times higher than the FFT resolution.

Ultrasonic pulsed wave range Doppler sensors provide application in various fields, e.g, intruder alarm systems or autonomous vehicle steering. The time-frequency methods commonly used in these sensors, however, inhere the problem that, due to the transducer's non-constant and direction-dependent transfer functions, the Doppler frequency cannot be determined with the high accuracy needed for such applications. The easiest way to improve the Doppler resolution is to reduce the signal bandwidth, but only at the expense of worse range resolution. A direction-dependent inverse filter technique is presented, which compensates the erroneous effects of the transfer function in the time-frequency analysis. An ultrasonic intruder alarm system determining location and velocity of persons in rooms serves as an example that the novel approach gives evidently better performance than conventional methods, resulting in both high velocity and range resolution.