Sensor fusion is crucial for an accurate and robust perception system on autonomous vehicles. Most existing datasets and perception solutions focus on fusing cameras and LiDAR. However, the collaboration between camera and radar is significantly under-exploited. Incorporating rich semantic information from the camera and reliable 3D information from the radar can achieve an efficient, cheap, and portable solution for 3D perception tasks. It can also be robust to different lighting or all-weather driving scenarios due to the capability of mmWave radars. In this paper, we introduce the CRUW3D dataset, including 66K synchronized and well-calibrated camera, radar, and LiDAR frames in various driving scenarios. Unlike other large-scale autonomous driving datasets, our radar data is in the format of radio frequency (RF) tensors that contain not only 3D location information but also spatio-temporal semantic information. This kind of radar format can enable machine learning models to generate more reliable object perception results after interacting and fusing the information or features between the camera and radar. We run several camera- and radar-based baseline methods for 3D object detection and multi-object tracking on our dataset. We hope the CRUW3D dataset will foster radar and multi-modal 3D perception research. CRUW3D is available at https://huggingface.co/datasets/uwipl/CRUW3D

Radar is usually more robust than the camera in severe driving scenarios, e.g., weak/strong lighting and bad weather. However, unlike RGB images captured by a camera, the semantic information from the radar signals is noticeably difficult to extract. In this paper, we propose a deep radar object detection network (RODNet), to effectively detect objects purely from the carefully processed radar frequency data in the format of range-azimuth frequency heatmaps (RAMaps). Three different 3D autoencoder based architectures are introduced to predict object confidence distribution from each snippet of the input RAMaps. The final detection results are then calculated using our post-processing method, called location-based non-maximum suppression (L-NMS). Instead of using burdensome human-labeled ground truth, we train the RODNet using the annotations generated automatically by a novel 3D localization method using a camera-radar fusion (CRF) strategy. To train and evaluate our method, we build a new dataset - CRUW, containing synchronized videos and RAMaps in various driving scenarios. After intensive experiments, our RODNet shows favorable object detection performance without the presence of the camera.

In this paper, we present an in-band on-channel (IBOC) broadcasting system suitable for digital audio and data broadcasting in FM and AM channels. The new system utilizes a combination of frequency hopping, LDPC coding, hierarchical modulation and band aggregation techniques to increase the data rate, improve spectrum efficiency, and at the same time, provide spectrum flexibility to meet the requirements of high quality audio and multimedia services. A prototype has been developed on a personal computer (PC) based software defined radio (SDR) platform. Numerical results and initial laboratory tests demonstrate significant performance advantages of the new design over existing systems such as the Digital Radio Mondiale (DRM) and Hybrid Digital (HD) radio.

Two types of wireless system imperfections, namely, frequency offset and in-phase/quadrature (I/Q) imbalance, are addressed in this paper. The I/Q imbalance in radio frequency (RF) direct-conversion not only introduces an unwanted in-band image interference but also degrades the accuracy of carrier estimation. Toward this end, we propose a pilot-based scheme for both frequency offset and I/Q imbalance compensation at the baseband. A low-cost nonlinear least squares (NLS) frequency estimator robust to the I/Q imbalance is developed. Also derived is an I/Q imbalance compensation structure that consists of two stages: a finite impulse response (FIR) filter that compensates the frequency dependent imbalance; and an asymmetric phase compensator that corrects the frequency independent error. The compensation coefficients are optimized by exploiting the phase rotation embedded in the pilot symbols. Both computer simulations and experiment results verify the effectiveness of the proposed scheme in various I/Q imbalance scenarios.

The paper describes a multicarrier terrestrial multimedia broadcasting (TMB) system that offers system performance and service versatility suitable for future digital broadcasting networks. The new system utilizes variable rate, variable size LDPC (low-density parity-check) codes to allow simultaneous transmission of different programs with diverse requirements. A prototype system is implemented based on the proposed protocol. Results from both lab tests and field trials demonstrate significant performance advantages of the new design over existing systems such as DVB-T.

Field-programmable gate arrays (FPGAs) have become an extremely popular implementation technology for custom hardware because they offer a combination of low cost and very fast turnaround. Because of their in-system reconfigurability, FPGAs have also been suggested as an efficient replacement for application-specific integrated circuits (ASICs) and digital signal processors (DSPs) for applications that require a combination of high performance, low cost, and flexibility. Unfortunately, the use of FPGAs in mobile embedded systems platforms is hampered by the very large overhead of FPGA-based architectures. Coarse-grained configurable architectures can reduce this overhead substantially by taking advantage of the application domain to specialize the reconfigurable architecture via coarse-grained components and interconnects. This paper presents the design and implementation of an OFDM receiver in the RaPiD reconfigurable architecture as a case study for comparing the relative cost and performance of ASIC, DSP, FPGA, and coarse-grained reconfigurable architectures. RaPiD is a coarse-grained reconfigurable architecture specialized to the domain of signal and image processing. The RaPiD architecture provides a reconfigurable pipelined datapath controlled by efficient reconfigurable control logic: We have implemented the computationally intensive parts of an OFDM receiver on the RaPiD architecture and have developed careful estimates of corresponding implementations in representative ASIC, DSP and FPGA technology. Our results show that, for this application, RaPiD fills the cost/performance gap between programmable DSP and ASIC architectures, achieving a factor of 6 better than a DSP implementation but a factor of 6 less than an ASIC implementation.

Two types of RF front-end imperfections in direct-conversion receivers, namely frequency offset and I/Q imbalance, are addressed. The I/Q imbalance not only introduces an unwanted image interference, but also degrades the accuracy of carrier estimation. We propose a pilot-based scheme for both frequency offset and I/Q imbalance compensation. A nonlinear least squares (NLS) frequency estimator, robust to the I/Q imbalance, is developed. Also derived is an I/Q compensation structure that consists of two stages: the frequency dependent imbalance is compensated with an FIR filter whereas the frequency independent part is corrected with an asymmetric phase compensator. The compensation coefficients are optimized by restoring the phase rotation embedded in the pilot symbols.