识别无人机的无人值守光电告警系统

无人机的高速发展不仅为人类的生活带来了便利,同样也引发了各类安全问题。为此,近年来,我国重大涉密工业场所和交通枢纽为保障运行安全,积极建设了针对无人机的安防系统。光电告警系统是最重要的安防系统组件之一,其目的是识别目标和取得法律证据。介绍了一种针对无人机的无人值守光电告警系统,能够同时输出可见光和红外图像信息,并且能够自动识别目标类型。这套系统已经在我国重要的工业场所中成功应用,并取得了阶段性的成果,其成功的运行为后续智能化光电告警平台的发展奠定了基础。     

光电池测光器在爆震波测量中的应用

爆震管内火焰的结构非常复杂且传播速度快,需要高精度、高响应时间的测试工具.本文在单爆震试验中采用光电池测光器和离子探针测量燃烧波.研究中以高频响压力传感器为参考依据,通过测量同一截面上压力传感器、光电池测光器和离子探针之间的触发顺序,以及光电池测光器与离子探针信号的持续时间,来验证光电池测光器在爆震波测量中的可行性与精度.结果表明:光电池测光器比离子探针更适合测量爆震管内火焰传播速度.      

Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking

Multi-Optical Theodolite Tracking systems (MOTTs) can stealthily extract the target’s status information from bearings only through non-contact measurement. The constrained MOTTs are partially compatible, yet many existing research works and results are based on the known model, ignoring its discrimination with the target maneuvering behavior pattern. To compensate for these mismatches, this paper develops a Measurement-driven Gauss-Hermite Particle Filter (MGHPF), which elegantly fuses the spatiotemporal constraints and its soft form to perform MOTT missions. Specifically, the target dynamic model and tracking algorithm are based on the target behavior pattern with the adaptive turn rate, fully exploiting the spatial epipolar geometry characteristics for each intersection measurement by a minimax strategy. Then, the center of the feasible area is approximated via the analytic coordinate transformation, and the latent samples are updated via the deterministic Gauss-Hermite integral method with the target’s predictive turn rate. Simultaneously, the effects of truncation correction and compensation feedback from the current measurement and historical estimation data are adaptively incorporated into the PF’s importance distribution to cover the mixture likelihood. Besides, an effective causality-invariant updating rule is provided to estimate the parameters of these soft spatiotemporal constrained MOTTs with convergence guarantees. Simulated and measured results show good agreement; compared with the state-of-the-art Multi-Model Rao-Blackwell Particle Filter (MMRBPF), the proposed MGHPF improves the filtering accuracy by 7.4%-34.7% and significantly reduces the computational load.