基于多域表征对齐融合的雷达目标无监督异常检测方法OA

With the evolution of precision-guided munitions and advances in radar sensing technologies,achieving ac⁃curate and efficient battle-damage assessment(BDA)based on changes in radar echoes has become a critical challenge in modern warfare.This task not only involves determining whether a target has been hit,but also detecting the hit location;its outcomes serve as an important basis for measuring the effectiveness of offense-defense engagements and provide guidance for optimizing tactics and weapon systems.This paper systematically investigates methods for damage assessment of air⁃craft targets in air-to-air combat scenarios and focuses on overcoming the following three key difficulties.First,obtaining ra⁃dar echo data before and after strikes in real battlefield environments is extremely difficult;how to construct damage scenar⁃ios via simulation and generate credible radar echo data is a fundamental problem this study must solve.Second,the hit loca⁃tions and their morphologies exhibit high randomness and uncertainty,making it difficult to build a comprehensive damage feature library and thereby limiting the applicability of supervised detection methods.Third,to meet real-time requirements,one-dimensional range profiles(high resolution range profile,HRRP)are commonly used for target state monitoring;how⁃ever,compared with inverse synthetic aperture radar(ISAR)images,HRRP lacks many stable structural features,increasing the difficulty of extracting intrinsic damage features.To address these challenges,this paper proposes an unsupervised anomaly-detection method for radar targets based on multi-domain representation alignment and fusion,aimed at achieving accurate and efficient assessment of damage effects on struck aircraft.Specifically,to tackle the difficulty of constructing damage scenarios and generating radar echo data,we propose a damage-scene simulation method based on Unity 3D,and generate target echo data by combining it with a radar point-scatterer-center model.To address the problem that an incom⁃plete damage feature library makes supervised information hard to utilize,we construct an unsupervised anomaly-detection framework based on reconstruction of normal signals,and introduce a self-attention mechanism to design an"identity-map⁃ping"cancellation module to suppress model degeneration and enhance damage recognition capability.To tackle the diffi⁃culty of extracting intrinsic features due to limited target structural information,we propose an unsupervised regularization method of multi-domain representation alignment and fusion:by introducing ISAR image features to augment structural in⁃formation in HRRP,and by designing a volume-metric function based on the Gram matrix to achieve robust domain align⁃ment between HRRP and ISAR images,thereby enhancing the mining of intrinsic damage features.From the perspective of Bayesian parameter optimization,reconstruction of normal signals provides an optimizable likelihood function for model pa⁃rameter learning,while multi-domain representation alignment and fusion correspond to an optimizable KL-divergence term;together they form a unified theoretical framework.We validate the proposed method on a self-developed simulated dataset of target damage.Experimental results indicate that,under test conditions with unsupervised signals and target ISAR images,the method can,relying solely on HRRP data from the normal state,effectively discover discriminative damage fea⁃tures and accurately distinguish between normal and damaged states.Furthermore,by transferring and fusing structural in⁃formation from target ISAR images,the model's area under the receiver operating characteristic curve(AUROC)on the damage-assessment task improves by 12.31 percentage points compared with the HRRP-only model.The above results vali⁃date that the proposed method possesses strong generalization capability and engineering application potential.