Autonomous Radar Scan-Mode Recognition Method Based on High-Dimensional Features and Random Forest

WU Kanghui; GUO Zixun; FAN Yifei; XIE Jian; TAO Mingliang

doi:10.11999/JEIT250985

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WU Kanghui, GUO Zixun, FAN Yifei, XIE Jian, TAO Mingliang. Autonomous Radar Scan-Mode Recognition Method Based on High-Dimensional Features and Random Forest[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250985

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WU Kanghui, GUO Zixun, FAN Yifei, XIE Jian, TAO Mingliang. Autonomous Radar Scan-Mode Recognition Method Based on High-Dimensional Features and Random Forest[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250985

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Autonomous Radar Scan-Mode Recognition Method Based on High-Dimensional Features and Random Forest

doi: 10.11999/JEIT250985 cstr: 32379.14.JEIT250985

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China

Funds: The National Natural Science Foundation of China(62301435, 62571444)

Received Date: 2025-09-25
Accepted Date: 2025-12-30
Rev Recd Date: 2025-12-30

Available Online: 2026-01-08

Abstract

Abstract

Objective Rapid and robust recognition of radar scanning modes under noncooperative electronic reconnaissance conditions is a prerequisite for threat assessment, resource scheduling, and countermeasure design. Mechanical Scanning (MST) and phased-array Electronic Scanning (EST) leave different physical imprints in the Time-Of-Arrival-Pulse Amplitude (TOA-PA) stream. However, their separability degrades under low Signal-to-Noise Ratio (SNR), nonstationary dwell scheduling, and jittered timing typical of dense electromagnetic environments. In this study, a physics-grounded, multi-domain feature framework coupled with a Random Forest (RF) classifier is developed to discriminate MST from EST using only intercepted TOA-PA sequences, without synchronization or prior emitter knowledge. Methods The reconnaissance reception chain is modeled, and Pulse Amplitude (PA) formation is formalized to clarify the association between antenna-pattern traversal and amplitude texture. From this physical perspective, seven complementary features are extracted across time, frequency, and graph structure: Coefficient of Variation (CV), Total Variation (TV), Gaussian Fitting Degree (GFD), Relative Width (RW), Spectral Flatness Measure (SFM), Global Clustering Coefficient (GCC) on a Horizontal Visibility Graph (HVG), and Normalized Degree Entropy (NDE). HVG construction preserves temporal order and reveals global structure induced by sequence shape. Features are computed per frame and concatenated into a seven-dimensional vector. The RF classifier is trained using bootstrap sampling and random-subspace splits, and inference is performed by majority voting over leaf-level posteriors. The full pipeline is summarized in Fig. 10. Computational complexity remains near linear: CV, TV, and RW scale as O(N); SFM is dominated by a single fast Fourier transform with O(Nlog₂N); and HVG-based features scale as O(Nlog₂N), satisfying low-latency constraints. Results and Discussions The dataset is constructed using paired MST and EST frames with time-of-arrival jitter of approximately 0.2% of the pulse repetition interval, additive white Gaussian noise across SNR levels, and realistic beam patterns that include sidelobes for both scanning schemes. Training spans 0$ \sim $30 dB, and testing covers –5.5$ \sim $29.5 dB in 5 dB steps. Using the proposed seven-feature vector, the RF classifier achieves an average accuracy of 97.59% across all SNRs and exceeds a support vector machine baseline with identical inputs at 96.01%. The largest margins are observed at low to mid SNR, as shown in Fig. 11. Single-feature analysis shows clear heterogeneity and complementarity. SFM provides the best single-feature performance at 0.916 1, followed by TV and NDE at 0.822 0 and 0.806 5, respectively. CV and GFD show intermediate performance at approximately 0.66, whereas RW and graph-based similarity measures are lower at approximately 0.56$ \sim $0.57. Joint multi-feature inputs increase accuracy to 0.975 9, yielding an absolute gain of 5.98 percentage points over the best single feature and reducing the error rate from 8.39% to 2.41%, corresponding to a relative reduction of approximately 71%. These improvements are summarized in Table 1 and Fig. 12. Runtime evaluation indicates that the dominant computational cost arises from the fast Fourier transform and HVG construction. A per-frame computation time of approximately 0.515 ms keeps the method suitable for on-orbit and embedded processing. The performance gains arise from the joint capture of four factors: smooth versus stepwise amplitude evolution represented by CV and TV; main-lobe morphology and time scale represented by GFD and RW, as illustrated in Fig. 4; spectral concentration versus dispersion represented by SFM, as illustrated in Fig. 5; and topology induced by alternating highs and lows under dwell switching represented by HVG clustering and entropy, as detailed in Fig. 7. Together, these factors stabilize the decision boundary against noise and dwell nonstationarity. Conclusions A physics-grounded, multi-domain feature framework combined with an RF discriminator is presented for radar scanning mode recognition under noncooperative conditions. The method is derived from intrinsic contrasts between MST, characterized by continuous, smooth, and quasi-periodic behavior, and phased-array EST, characterized by dwell-based, jumping, and nonstationary behavior. A TOA-PA signal model consistent with engineering practice is constructed, and complementary features are designed across time (CV, TV), main-lobe morphology (GFD, RW), frequency (SFM), and graph structure (GCC, NDE). The RF classifier applies bootstrap sampling and random subspaces to reduce variance and mitigate overfitting, enabling robust decisions. Across detection scenarios from –5 dB to 30 dB, an average accuracy of 97.59% is obtained. Compared with schemes based on single-domain features or a limited feature set, the proposed framework provides higher recognition stability under low-SNR and other challenging disturbance conditions.
- Radar scanning mode,
- Feature extraction,
- Random forest,
- Horizontal visibility graph

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References(20)

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