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

YE Juhang; DUAN Jia; ZHANG Lei

doi:10.11999/JEIT250689

Volume 47 Issue 12

Dec. 2026

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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, 2025, 47(12): 4896-4904. 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, 2025, 47(12): 4896-4904. 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

Funds: The National Natural Science Foundation of China(62201623), The Natural Science Foundation of Guangdong Province (2025A1515010242)

Received Date: 2025-07-23
Accepted Date: 2025-11-13
Rev Recd Date: 2025-11-13

Available Online: 2025-11-17

Publish Date: 2025-12-10

Abstract

Abstract

Objective Space activities continue to expand, and Space Situational Awareness (SSA) is required to support collision avoidance and national security. A core task is attitude classification of space targets to interpret states and predict possible behavior. Current classification strategies mainly depend on Ground-Based Inverse Synthetic Aperture Radar (GBISAR). Model-driven methods require accurate prior modeling and have high computational cost, whereas data-driven methods such as deep learning require large annotated datasets, which are difficult to obtain for space targets and therefore perform poorly in small-sample conditions. To address this limitation, a Fuzzy Attitude Classification (FAC) method is proposed that integrates temporal motion modeling with fuzzy set theory. The method is designed as a training-free, real-time classifier for rapid deployment under data-constrained scenarios. 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 evolution is represented as Euler rotations of the BFRF relative to the UNW system. The periodic 3D rotation is projected onto the 2D Range-Doppler plane as circular keypoint trajectories. Fourier series analysis is then applied to convert the motion into One-Dimensional (1D) cosine features, where phase represents angular velocity and amplitude reflects motion magnitude. A 10-point annotation model is employed to describe targets, and dimensionless roll, pitch, and yaw feature vectors are constructed. For classification, magnitude- and angle-based decision rules are defined and processed using a softmax membership function, which incorporates feature variance to compute fuzzy membership degrees. The algorithm operates directly on keypoint sequences, requires no training, and maintains linear computational complexity O(n), enabling real-time execution. Results and Discussions The FAC method is evaluated using a Ku-band GBISAR simulated dataset of a spinning target. The dataset contains 36 sequences, each composed of 36 frames with a resolution of 512×512 pixels and is partitioned into a reference set and a testing set. Although raw keypoint trajectories appear disordered (Fig. 4(a)), the engineered features form clear clusters (Fig. 4(b)), and the variance of the defined criteria reflects motion significance (Fig. 4(c)). Robustness is confirmed: across nine imaging angles, classification consistency remains 100% within a 0.0015 tolerance (Fig. 5(a)). Under noise conditions, consistency is maintained from 10 dB to 1 dB signal-to-noise ratio (Fig. 5(b)). When frames are removed, 90% consistency is retained at a 0.03 threshold, and six frames are identified as the minimum number required for effective classification (Fig. 5(c)). Benchmark comparisons indicate that FAC outperforms Hidden Markov Models (HMM) and Convolutional Neural Networks (CNN), preserving accuracy under noise (Fig. 6(a)), sustaining stability under frame loss where HMM degrade to random behavior (Fig. 6(b)), and achieving significantly lower processing time than both benchmarks (Fig. 6(c)). Conclusions An FAC method that integrates motion modeling with fuzzy reasoning is presented for small-sample space target recognition. 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 processing. Simulation tests confirm robustness across observation angles, Signal-to-Noise Ratios (SNR), and frame availability. Benchmark comparisons demonstrate higher accuracy, stability, and computational efficiency relative to HMM and CNN. The FAC method provides a feasible solution for real-time attitude classification in data-constrained scenarios. Future work will extend the approach to multi-axis tumbling and validation using measured data, with potential integration of multimodal observations to improve adaptability.
- Attitude classification,
- Ground-Based Inverse Synthetic Aperture Radar (GBISAR),
- Space target,
- Temporal motion modeling,
- Fuzzy theory

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References(25)

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