Automotive Radar Multi-Frame Track-Before-Detect Algorithm Considering Self-Positioning Errors

This paper presents a method for the joint detection and tracking of weak targets in automotive radars using the multi-frame track-before-detect (MF-TBD) procedure. Generally, target tracking in automotive radars is challenging due to radar field of view (FOV) misalignment, nonlinear coordinate conversion, and self-positioning errors of the ego-vehicle, which are caused by platform motion. These issues significantly hinder the implementation of MF-TBD in automotive radars. To address these challenges, a new MF-TBD detection architecture is first proposed. It can adaptively adjust the detection threshold value based on the existence of moving targets within the radar FOV. Since the implementation of MF-TBD necessitates the inclusion of position, velocity, and yaw angle information of the ego-vehicle, each with varying degrees of measurement error, we further propose a multi-frame energy integration strategy for moving-platform radar and accurately derive the target energy integration path functions. The self-positioning errors of the ego-vehicle, which are usually not considered in some previous target tracking approaches, are well addressed. Numerical simulations and experimental results with real radar data demonstrate large detection and tracking gains over standard automotive radar processing in weak target environments.

Joint Beamforming and Antenna Position Design for IRS-Aided Multi-User Movable Antenna Systems

Intelligent reflecting surface (IRS) and movable antenna (MA) technologies have been proposed to enhance wireless communications by creating favorable channel conditions. This paper investigates the joint beamforming and antenna position design for an MA-enabled IRS (MA-IRS)-aided multi-user multiple-input single-output (MU-MISO) communication system, where the MA-IRS is deployed to aid the communication between the MA-enabled base station (BS) and user equipment (UE). In contrast to conventional fixed position antenna (FPA)-enabled IRS (FPA-IRS), the MA-IRS enhances the wireless channel by controlling the positions of the reflecting elements. To verify the system's effectiveness and optimize its performance, we formulate a sum-rate maximization problem with a minimum rate threshold constraint for the MU-MISO communication. To tackle the non-convex problem, a product Riemannian manifold optimization (PRMO) method is proposed for the joint design of the beamforming and MA positions. Specifically, a product Riemannian manifold space (PRMS) is constructed and the corresponding Riemannian gradient is derived for updating the variables, and the Riemannian exact penalty (REP) method and a Riemannian Broyden-Fletcher-Goldfarb-Shanno (RBFGS) algorithm is derived to obtain a feasible solution over the PRMS. Simulation results demonstrate that compared with the conventional FPA-IRS-aided MU-MISO communication, the reflecting elements of the MA-IRS can move to the positions with higher channel gain, thus enhancing the system performance. Furthermore, it is shown that integrating MA with IRS leads to higher performance gains compared to integrating MA with BS.