摘要
针对复杂电磁环境下无人机目标低可观测性、高机动性带来的检测难题,本文提出一种基于启发式搜索的极化雷达无人机检测方法。通过建立融合运动学约束、加速度约束与极化能量一致性约束的启发式搜索框架,有效解决了传统多帧检测方法计算复杂度高、机动适应性差的瓶颈。首先,基于雷达数据经短时傅里叶变换后的时间-距离-速度三维数据矩阵构建三维状态空间。其次,设计多约束融合的启发式函数:运动学约束基于运动学方程建立速度-距离映射模型,推导相邻帧间可达区域以压缩搜索空间;加速度约束引入加速度惩罚项抑制非物理机动轨迹;极化能量一致性约束通过结合双极化通道能量聚焦特性,构建几何平均与最小值双重约束的交叉验证机制。最后,通过线性加权将三类约束整合为统一的启发式函数评价准则,并采用贪心最佳优先搜索方法实现动态剪枝与路径优化。仿真实验表明,在信噪比5 dB以上时,本方法对匀速、匀加速及变加速三类运动模式的检测率均超过80%,较传统恒虚警率检测方法显著提升;目标跟踪航迹正确率大于80%时所需的信噪比较传统方法降低5 dB以上。经实测数据验证,针对固定翼无人机和旋翼无人机,本方法在信噪比起伏条件下仍保持连续轨迹检测能力。仿真分析与实测结果表明,所提出的多约束启发式搜索机制通过物理规律与观测数据的协同优化,实现了复杂机动无人机的有效检测。
To address the detection challenges posed by the low observability and high maneuverability of unmanned aerial vehicle(UAV)targets in complex electromagnetic environments,we propose a heuristic search-based detection method in polarimetric radar for UAV.By establishing a heuristic search framework that integrates kinematic,acceleration,and polarimetric-energy-consistency constraints,this approach effectively overcomes the limitations of high computational complexity and poor adaptability to maneuvering inherent in conventional multi-frame detection methods.First,we constructed a three-dimensional state space based on the time-range-velocity data matrix derived from shorttime Fourier transform-processed radar signals.Second,we designed a multi-constraint heuristic function.We established kinematic constraints on a velocity-range mapping model through kinematic equations to derive inter-frame reachable regions for search space compression.We also incorporated acceleration constraints including penalty terms to suppress non-physical maneuver trajectories.And we set polarimetric-energy-consistency constraints to realize a cross-validation mechanism combining geometric mean and minimum value criteria by leveraging the energy focusing characteristics of dual-polarization channels.Finally,we unified these three constraints into a heuristic-function-based evaluation criterion via linear weighting,with dynamic pruning and path optimization achieved through the greedy best-first search method.Simulation results demonstrate that the proposed method achieves detection rates exceeding 80%for constant-velocity targets,uniformly accelerated targets,and time-varying accelerated targets when the signal-to-noise ratio exceeds 5 dB,significantly outperforming traditional detection methods featuring constant false alarm rate.Furthermore,the required signal-to-noise ratio for attaining trajectory correctness rates greater than 80%was reduced by over 5 dB compared to conventional approaches.Validation using real measured data demonstrates continuous trajectory detection capability for both fixed-wing and rotary-wing UAV under fluctuating signal-to-noise ratio conditions.Both simulation analysis and real measured data validation confirm that the proposed multi-constraint heuristic search mechanism enables effective detection of complex maneuvering UAV through synergistic optimization of physical principles and observational data.
作者
杨博宇
杨勇
李永祯
李泽榕
周保一
YANG Boyu;YANG Yong;LI Yongzhen;LI Zerong;ZHOU Baoyi(College of Electronic Science and Technology,National University of Defense Technology,Changsha,Hunan 410073,China)
出处
《信号处理》
北大核心
2025年第5期867-885,共19页
Journal of Signal Processing
基金
国家自然科学基金(62171447)。
关键词
多帧检测
启发式搜索
无人机
目标检测
极化雷达
multi-frame detection
heuristic search
unmanned aerial vehicle
target detection
polarimetric radar
