Radar has become an essential sensor for autonomous navigation, especially in challenging environments where camera and LiDAR sensors fail. 4D single-chip millimeter-wave radar systems, in particular, have drawn increasing attention thanks to their ability to provide spatial and Doppler information with low hardware cost and power consumption. However, most single-chip radar systems using traditional signal processing, such as Fast Fourier Transform, suffer from limited spatial resolution in radar detection, significantly limiting the performance of radar-based odometry and Simultaneous Localization and Mapping (SLAM) systems. In this paper, we develop a novel radar signal processing pipeline that integrates spatial domain beamforming techniques, and extend it to 3D Direction of Arrival estimation. Experiments using public datasets are conducted to evaluate and compare the performance of our proposed signal processing pipeline against traditional methodologies. These tests specifically focus on assessing structural precision across diverse scenes and measuring odometry accuracy in different radar odometry systems. This research demonstrates the feasibility of achieving more accurate radar odometry by simply replacing the standard FFT-based processing with the proposed pipeline. The codes are available at GitHub**https://github.com/SenseRoboticsLab/DBE-Radar.

The paradigm of unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) has emerged as an effective scheme for handling intensive tasks in heterogeneous networks. In this work, we consider a user-equipment-rich city network scenario. Due to the limited user equipments (UEs) resources and base station (BS) coverage, we utilise multiple-UAV-assisted UEs and partial offloading to handle the tasks. Meanwhile, considering the impact of task diversity on the quality of service (QoS) of the system, we design an integrated scheme that combines improved clustering techniques and deep reinforcement learning (DRL) for dynamic resource scheduling. Firstly, a random forest-based clustering algorithm (RFCA) is used to cluster UEs according to the service requirements (SR) of tasks, as a way to reduce the complexity of task processing and user association. Then a DRL-based computational offloading and bandwidth allocation algorithm (DCOBA) is used to improve the QoS by jointly optimising UAV-user associations, offloading ratios, and bandwidth allocation to reduce the system latency and energy consumption. Finally, experimental simulation data shows that our scheme can better optimise the Qos compared to traditional schemes.