Digital radars with low-resolution analog-to-digital converters (ADCs) can reduce digital processing complexity and power consumption but suffer from limited dynamic range. The poor dynamic range causes high radar cross-section (RCS) targets to mask low-RCS ones. To mitigate this issue, we propose operating the ADC at a high resolution during the initial slowtime slot of each radar frame. The high-resolution measurements are used to estimate the range and RCS of dominant targets, which, along with their known Doppler statistics, are used to construct a dither signal. This dither signal is then employed to acquire low-resolution ADC measurements in the subsequent slow-time slots. With the proposed receiver architecture, our method suppresses strong target returns in the low-resolution measurements, effectively unmasking weak targets. Simulations demonstrate significant improvements in target detection and reduced normalized mean square error in radar channel estimation compared to existing benchmarks.

Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

Block compressed sensing (BCS) alleviates the high storage and memory complexity with standard CS by dividing the sparse recovery problem into sub-problems. This paper presents a Welch bound-based guarantee on the reconstruction error with BCS, revealing that sparse recovery deteriorates with more partitions. To address this performance loss, we propose a data-driven BCS technique that leverages correlation across signal partitions. Our method surpasses classical BCS in moderate SNR regimes, with a modest increase in storage and computational complexities.

Phase jitter at oscillators in high-frequency wireless systems perturbs the phase of the acquired channel measurements. As a result, standard sparse channel estimation algorithms that ignore phase errors fail. In this paper, we consider a frame structure in which channel measurements are acquired over two packets. Our model assumes that the phase errors are nearly constant over a packet and they change considerably across the packets, leading to partially coherent channel measurements. In this paper, we develop a message-passing-based technique that leverages the partially coherent structure in the measurements for sparse channel estimation robust to unknown phase offset across the packets. Simulation results show that our approach achieves a lower mean-squared error in the reconstructed channel than comparable benchmarks.

Light detection and ranging (LiDAR) is used in robots and in automotives to obtain the perception of the surrounding environment. Traditional spinning LiDARs scan the environment uniformly along all angular directions by operating at a constant rotational speed, with fixed sensing parameters throughout a rotation. Such a sensing approach, however, is suboptimal when information about static obstacles in the environment is available at the LiDAR. In this work, we introduce ELLAS, a first-of-its-kind spinning LiDAR system that dynamically adapts its range and resolution over the field of view. This adaptation is achieved by optimizing the ranging parameters at the LiDAR and the instantaneous rotational speed of the spinning platform to the location of static objects in scene topology maps. With the optimized settings, ELLAS results in a longer range along directions where static obstacles are farther away and achieves a higher angular resolution around those directions.

Channel estimation can lead to a substantial training overhead in millimeter wave (mmWave) and terahertz (THz) systems employing large arrays. Prior work has leveraged channel sparsity at these frequencies to reduce this overhead. Most of the sparsity-aware algorithms, however, assume perfect phase coherence in the channel measurements, which is disrupted due to phase noise. Due to the errors induced by phase noise, standard sparse channel estimation algorithms assuming perfect phase coherence can fail. In this paper, we consider a frame structure in which the channel measurements are acquired over multiple packets. Our model assumes that the phase errors remain constant within a packet and vary considerably across different packets, leading to partially coherent channel measurements. We develop a message passing-based technique for sparse channel estimation under such partially coherent phase errors and show that our approach achieves a lower channel reconstruction error than comparable benchmarks.

We address the problem of estimating a binary occupancy grid map by fusing point cloud data from radar and LiDAR sensors for automotive driving perception. To achieve this, we introduce two measurement models for fusion and formulate occupancy mapping as sparse vector reconstruction from the set of radar and LiDAR measurements. The first model, called common sparse fusion, jointly estimates a common map from all sensor measurements. The second model, called common innovative sparse fusion, assumes a shared map and an innovation component (error collector) for each sensor modality’s measurements. This approach enhances the robustness of occupancy map estimation against potential sensor mismatch and calibration errors, and inconsistencies between the two modalities. We use the pattern-coupled sparse Bayesian learning (PCSBL) algorithm to recover maps, leveraging the inherent sparsity and spatial dependencies in automotive occupancy maps. Numerical experiments on the public RADIATE dataset show that our feature-level fusion models outperform single-modality mapping and decision-level fusion models in detecting drivable areas and targets. Furthermore, statistical results with corrupted LiDAR data establish that our common innovative sparse fusion model is robust against unreliable sensor data

Automotive LiDARs typically have a uniform scanning range over their field of view (FoV). Such a range profile does not account for the varying risk of misdetecting targets in different regions. For instance, prioritizing crosswalks in a LiDAR scan is crucial, as the financial consequences of missing a pedestrian far exceed that of overlooking a distant vehicle. In this paper, we construct a spatial risk map that quantifies the risk of misdetecting targets across different regions around the vehicle. Our risk map incorporates lane semantics, knowledge about previously identified objects, and their potential trajectories. We use this risk map to adapt the LiDAR's scanning range over different sectors in its FoV. Simulations on nuScenes episodes demonstrate that our misdetection risk-aware design reduces the effective risk by about 40% compared to a standard LiDAR.

Beam acquisition is key in enabling millimeter wave and terahertz radios to achieve their capacity. Due to the use of large antenna arrays in these systems, the common exhaustive beam scanning results in a substantial training overhead. Prior work has addressed this issue by developing compressive sensing (CS)-based methods which exploit channel sparsity for faster beam acquisition. Unfortunately, most CS techniques employ wide beams and suffer from a low signal-to-noise ratio (SNR) in the channel measurements. To solve this challenge, we develop an IEEE 802.11ad/ay compatible technique that takes an in-sector approach for CS. In our method, the angle domain channel is partitioned into several sectors, and the channel within the best sector is estimated and then used for beamforming. The essence of our framework lies in the construction of a low-resolution beam codebook to identify the best sector and in the design of a CS matrix optimized for in-sector channel estimation. Our beam codebook illuminates distinct non-overlapping sectors and can be realized with low-resolution phased arrays. We show that the proposed codebook results in a higher received SNR than the state-of-the-art sector sweep codebooks. Furthermore, our optimized CS matrix achieves a better in-sector channel reconstruction and a higher achievable rate than comparable benchmarks.

We tackle the problem of estimating a binary occupancy grid map by fusing point cloud data from LiDAR and radar sensors for automotive driving perception. To this end, we introduce two sparsity measurement models for fusion, formulating occupancy mapping as a sparse binary vector reconstruction problem. The first model jointly estimates a common map from all measurements, while the second assumes a shared map and an innovation component for each modality's measurements. We use the pattern-coupled sparse Bayesian learning algorithm to recover maps, leveraging the inherent sparsity and spatial dependencies in automotive occupancy maps. Numerical experiments on the RADIATE public dataset show that our fusion-based approach improves mapping accuracy compared to single-modality and high-level fusion mapping algorithms.

Occupancy grid maps provide information about obstacles and available free space in the environment and are crucial in automotive driving applications. An occupancy map is constructed using point cloud data from sensor modalities such as light detection and ranging (LiDAR) and radar used for automotive perception. In this article, we formulate the problem of estimating the occupancy grid map using sensor point cloud data as a sparse binary occupancy value reconstruction problem. Our proposed occupancy grid estimation method, based on pattern-coupled sparse Bayesian learning (PC-SBL), leverages the sparsity and spatial dependencies inherent in occupancy maps typically encountered in automotive scenarios. The proposed method shows enhanced detection capabilities compared to two benchmark methods based on performance evaluation with scenes from the nuScenes and RADIal public datasets.

Millimeter-wave radar is a common sensor modality used in automotive driving for target detection and perception. These radars can benefit from side information on the environment being sensed, such as lane topologies or data from other sensors. Existing radars do not leverage this information to adapt waveforms or perform prior-aware inference. In this paper, we model the side information as an occupancy map and design transmit beamformers that are customized to the map. Our method maximizes the probability of detection in regions with a higher uncertainty on the presence of a target. Simulation results on the nuScenes dataset show that the designed beamformer achieves substantially higher detection rates than a conventional omnidirectional beamformer for the same transmitted power.

This paper develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems, the phase errors within a beam refinement protocol packet are almost the same, while the errors across different packets are substantially different. Consequently, standard sparsity-aware algorithms, which ignore phase errors, fail when channel measurements are acquired over multiple beam refinement protocol packets. We present a novel algorithm called partially coherent matching pursuit for sparse channel estimation under practical phase noise perturbations. Our method iteratively detects the support of sparse signal and employs alternating minimization to jointly estimate the signal and the phase errors. We numerically show that our algorithm can reconstruct the channel accurately at a lower complexity than the benchmarks.

The problem of estimating occupancy grids to support automotive driving applications using LiDAR sensor point clouds is considered. We formulate the problem as a sparse binary occupancy value reconstruction problem. Our proposed occupancy grid estimation method is based on pattern-coupled sparse Bayesian learning and exploits the inherent sparsity and spatial occupancy dependencies in LiDAR sensor measurements. The proposed method demonstrates enhanced detection capabilities compared to commonly used benchmark methods, as observed through testing on scenes from the nuScenes dataset.

Millimeter wave (mmWave) technology can achieve high-speed communication due to the large available spectrum. Furthermore, the use of directional beams in mmWave system provides a natural defense against physical layer security attacks. In practice, however, the beams are imperfect due to mmWave hardware limitations such as the low-resolution of the phase shifters. These imperfections in the beam pattern introduce an energy leakage that can be exploited by an eavesdropper. To defend against such eavesdropping attacks, we propose a directional modulation-based defense technique where the transmitter applies random circulant shifts of a beamformer. We show that the use of random circulant shifts together with appropriate phase adjustment induces (APN) in the directions different from that of the target receiver. Our method corrupts the phase at the eavesdropper without affecting the communication link of the target receiver. We also experimentally verify the APN induced due to circulant shifts, using channel measurements from a 2-bit mmWave phased array testbed. Using simulations, we study the performance of the proposed defense technique against a greedy eavesdropping strategy in a vehicle-to-infrastructure scenario. The proposed technique achieves better defense than the antenna subset modulation, without compromising on the communication link with the target receiver.

Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS). Prior work on OMP, however, has only provided reconstruction guarantees under the assumption that the columns of the CS matrix have equal norms, which is unrealistic in many practical CS applications due to hardware constraints. In this paper, we derive sparse recovery guarantees with OMP, when the CS matrix has unequal column norms. Finally, we show that CS matrices whose column norms are comparable achieve tight guarantees for the successful recovery of the support of a sparse signal and a low mean squared error in the estimate.

Phased arrays, commonly used in IEEE 802.11ad and 5G radios, are capable of focusing radio frequency signals in a specific direction or a spatial region. Beamforming achieves such directional or spatial concentration of signals and enables phased array-based radios to achieve high data rates. Designing beams for millimeter wave and terahertz communication using massive phased arrays, however, is challenging due to hardware constraints and the wide bandwidth in these systems. For example, beams which are optimal at the center frequency may perform poor in wideband communication systems where the radio frequencies differ substantially from the center frequency. The poor performance in such systems is due to differences in the optimal beamformers corresponding to distinct radio frequencies within the wide bandwidth. Such a mismatch leads to a misfocus effect in near-field systems and the beam squint effect in far-field systems. In this paper, we investigate the misfocus effect and propose InFocus, a low complexity technique to construct beams that are well suited for massive wideband phased arrays. The beams are constructed using a carefully designed frequency modulated waveform in the spatial dimension. InFocus mitigates beam misfocus and beam squint when applied to near-field and far-field systems.

Eavesdropping attacks are a severe threat to millimeter-wave (mmWave) networks that use low-resolution phased arrays. Although directional beamforming in mmWave phased arrays provides natural defense against eavesdropping, the use of low-resolution phase shifters induces energy leakage into unintended directions. This energy leakage can be exploited by the adversaries. In this paper, we propose a directional modulation (DM)-based defense against eavesdropping attacks on low-resolution phased arrays. Our defense technique applies random circulant shifts to the beamformer for every symbol transmission. By appropriately adjusting the phase of the transmitted symbol, the transmitter (TX) can maintain a high-quality link with the receiver while corrupting the symbols transmitted along unintended directions. We theoretically analyze the secrecy mutual information (SMI) achieved by the proposed defense mechanism and show that our defense induces artificial phase noise (APN) along unintended directions, which increases the SMI of the system. Finally, we numerically show the superiority of the proposed defense technique over the state-of-the-art defense techniques.

Phased arrays in near-field communication allow the transmitter to focus wireless signals in a small region around the receiver. Proper focusing is achieved by carefully tuning the phase shifts and the polarization of the signals transmitted from the phased array. In this paper, we study the impact of polarization on near-field focusing and investigate the use of dynamic polar-ization control (DPC) phased arrays in this context. Our studies indicate that the optimal polarization configuration for near-field focusing varies spatially across the antenna array. Such a spatial variation motivates the need for DPC phased arrays which allow independent polarization control across different antennas. We show using simulations that DPC phased arrays in the near-field achieve a higher received signal-to-noise ratio than conventional switched- or dual-polarization phased arrays.

Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams. To address this problem, we develop an in-sector CS-based mmWave channel estimation technique that focuses energy on a sector in the angle domain. Specifically, we construct a new class of structured CS matrices to estimate the channel within the sector of interest. To this end, we first determine an optimal sampling pattern when the number of measurements is equal to the sector dimension and then use its subsampled version in the sub-Nyquist regime. Our approach results in low aliasing artifacts in the sector of interest and better channel estimates than benchmark algorithms.