An array of two periodic leaky-wave antennas with sum and difference beam scanning for application in target detection and tracking

An array of two substrate-integrated waveguide(SIW) periodic leaky-wave antennas(LWAs) with sum and difference beam scanning is proposed for application in target detection and tracking. The array is composed of two periodic LWAs with different periods, in which each LWA generates a narrow beam through the n=-1 space harmonic. Due to the two different periods for the two LWAs, two beams with two different directions can be realized, which can be combined into a sum beam when the array is fed in phase or into a difference beam when the array is fed 180?out of phase. The array integrated with 180?hybrid is designed, fabricated, and measured.Measurement results show that the sum beam can reach a gain up to 15.9 dBi and scan from-33.4?to 20.8?. In the scanning range, the direction of the null in the difference beam is consistent with the direction of the sum beam,with the lowest null depth of-40.8 dB. With the excellent performance, the antenna provides an alternative solution with low complexity and low cost for target detection and tracking.

Design of a wide-field target detection and tracking system using the segmented planar imaging detector for electro-optical reconnaissance

Detecting and tracking multiple targets simultaneously for space-based surveillance requires multiple cameras,which leads to a large system volume and weight. To address this problem, we propose a wide-field detection and tracking system using the segmented planar imaging detector for electro-optical reconnaissance. This study realizes two operating modes by changing the working paired lenslets and corresponding waveguide arrays： a detection mode and a tracking mode. A model system was simulated and evaluated using the peak signal-to-noise ratio method. The simulation results indicate that the detection and tracking system can realize wide-field detection and narrow-field, multi-target, high-resolution tracking without moving parts.

Surrounding Objects Detection and Tracking for Autonomous Driving Using LiDAR and Radar Fusion

Radar and LiDAR are two environmental sensors commonly used in autonomous vehicles,Lidars are accurate in determining objects’positions but significantly less accurate as Radars on measuring their velocities.However,Radars relative to Lidars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution.In order to compensate for the low detection accuracy,incomplete target attributes and poor environmental adaptability of single sensors such as Radar and LiDAR,in this paper,an effective method for high-precision detection and tracking of surrounding targets of autonomous vehicles.By employing the Unscented Kalman Filter,Radar and LiDAR information is effectively fused to achieve high-precision detection of the position and speed information of targets around the autonomous vehicle.Finally,the real vehicle test under various driving environment scenarios is carried out.The experimental results show that the proposed sensor fusion method can effectively detect and track the vehicle peripheral targets with high accuracy.Compared with a single sensor,it has obvious advantages and can improve the intelligence level of autonomous cars.