ISAR Sequence Motion Modeling and Fuzzy Attitude Classification Method for Small Sample Space Target

YE Juhang; DUAN Jia; ZHANG Lei

doi:10.11999/JEIT250689

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YE Juhang, DUAN Jia, ZHANG Lei. ISAR Sequence Motion Modeling and Fuzzy Attitude Classification Method for Small Sample Space Target[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250689

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YE Juhang, DUAN Jia, ZHANG Lei. ISAR Sequence Motion Modeling and Fuzzy Attitude Classification Method for Small Sample Space Target[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250689

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ISAR Sequence Motion Modeling and Fuzzy Attitude Classification Method for Small Sample Space Target

doi: 10.11999/JEIT250689 cstr: 32379.14.JEIT250689

School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

Accepted Date: 2025-11-13
Rev Recd Date: 2025-11-13

Available Online: 2025-11-17

Abstract

Abstract

Objective With the intensification of space activities, Space Situational Awareness (SSA) is required to ensure national security and collision avoidance. A key task is the classification of space target attitudes to interpret states and predict behavior. Current approaches mainly rely on Ground-Based Inverse Synthetic Aperture Radar (GBISAR), which exhibit certain limitations. Model-driven methods rely on accurate prior models and involve high computational costs, while data-driven methods such as deep learning depend on large annotated datasets, which are difficult to obtain for space targets, and thus perform poorly in small-sample scenarios. To address this, a fuzzy attitude classification (FAC) method is proposed, which integrates temporal motion modeling with fuzzy set theory. The method is designed as a training-free and real-time classifier for rapid deployment under data-constrained conditions. Methods The method establishes a mapping between three-dimensional (3D) attitude dynamics and two-dimensional (2D) ISAR features through a framework combining the Horizon Coordinate System (HCS), the UNW orbital system, and the Body-Fixed Reference Frame (BFRF). Attitude changes are modeled as Euler rotations of BFRF relative to UNW. The periodic 3D rotation is projected onto the 2D Range-Doppler plane as circular keypoint trajectories. Fourier series analysis is then used to decompose the motion into one-dimensional (1D) cosine features, where phase encodes angular velocity and amplitude indicates motion magnitude. A 10-point annotation model is employed to represent targets, and dimensionless roll, pitch, and yaw feature vectors are derived. For classification, magnitude- and angle-based criteria are defined and processed by a softmax membership function, which incorporates variance across the sequence to compute fuzzy membership degrees. The algorithm operates directly on keypoint sequences, avoids training, and maintains linear computational complexity O(n), enabling real-time application. Results and Discussions The FAC method is evaluated on a Ku-band GBISAR simulated dataset of a spinning target. The dataset consists of 36 sequences, each with 36 frames of 512×512 images, devided as reference set as well as testing set. While raw keypoint tracks appear disordered (Fig. 4(a)), the engineered features form clustered patterns (Fig. 4(b)). The variance of the criteria effectively represents motion significance (Fig. 4(c)). Robustness is demonstrated: across nine imaging angles, classification consistency remains 100% within a 0.04 tolerance (Fig. 5(a)). Under noise, consistency is preserved from 10 dB to 1 dB SNR (Fig. 5(b)). With frame loss, 90% consistency is sustained at a 0.1 threshold, with six frames being the minimum for effective classification (Fig. 5(c)). Benchmark comparisons show that FAC outperforms HMM and CNN, maintaining accuracy under noise (Fig. 6(a)), stability under frame loss where HMM degrades to random behavior (Fig. 6(b)), and achieving much lower processing time than both HMM and CNN (Fig. 6(c)). Conclusions A fuzzy attitude classification method combining motion modeling and fuzzy reasoning is presented for small-sample space target classification. By mapping multi-coordinate kinematics into interpretable cosine features, the method reduces dependence on prior models and large datasets, while achieving training-free, linear-time operation. Simulations verify robustness across observation angles, SNR levels, and frame availability. Benchmark results confirm superior accuracy, stability, and efficiency compared with HMM and CNN. The FAC method therefore provides a practical solution for real-time, small-sample attitude classification. Future work will extend the framework to multi-axis tumbling and validation using measured data, with potential integration of multi-modal observations to further enhance adaptability.
- attitude classification,
- ground-based inverse synthetic aperture radar imaging,
- space target,
- temporal motion modeling,
- fuzzy theory

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References(25)

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