Spatial-domain Anti-jamming for Unmanned Systems Under Limited Prior Information

PAN Zihao; ZHANG Bangning; ZHEN Pan; ZHU Bowen; WANG Ning; GUO Daoxing

doi:10.11999/JEIT260296

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PAN Zihao, ZHANG Bangning, ZHEN Pan, ZHU Bowen, WANG Ning, GUO Daoxing. Spatial-domain Anti-jamming for Unmanned Systems Under Limited Prior Information[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260296

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PAN Zihao, ZHANG Bangning, ZHEN Pan, ZHU Bowen, WANG Ning, GUO Daoxing. Spatial-domain Anti-jamming for Unmanned Systems Under Limited Prior Information[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260296

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Spatial-domain Anti-jamming for Unmanned Systems Under Limited Prior Information

doi: 10.11999/JEIT260296 cstr: 32379.14.JEIT260296

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

Received Date: 2026-03-17
Accepted Date: 2026-06-29
Rev Recd Date: 2026-06-26

Available Online: 2026-07-13

Abstract

Abstract

Objective Unmanned systems play an increasingly important role in emergency response, public safety, intelligent transportation, and other mission-critical applications. Reliable communications in complex electromagnetic environments are essential for autonomous operation. However, communication links are directly exposed to open, non-cooperative electromagnetic environments and are therefore vulnerable to intentional jamming and unintentional interference. In practical scenarios, prior information regarding the desired signal, jamming sources, and multipath propagation is often unavailable, substantially degrading the performance of conventional spatial-domain anti-jamming methods. To address this challenge, this paper proposes a spatial-domain anti-jamming framework for unmanned systems operating under limited prior information. Methods The proposed method first applies a spatial smoothing algorithm to the received signals to decorrelate coherent multipath components. Capon spatial spectrum estimation is then performed to detect the Direction Of Arrival (DOA) of potential incident signals. Spectrum peaks corresponding to individual incident signals are subsequently identified. A Covariance Matrix Reconstruction (CMR)-based beamforming algorithm is then applied by traversing all detected spectrum peaks to sequentially extract the signal associated with each peak, thereby separating the mixed signals. After signal separation, a signal classification method based on spectral similarity and time delay is employed. Kullback-Leibler (KL) divergence between the spectrum of each separated signal and the reference spectrum is calculated to identify jamming signals. The remaining communication signals are further classified into direct-path and multipath signals according to their relative time delays. Finally, different processing strategies are applied according to the identified signal type. Specifically, multipath signals are either suppressed as interference or coherently combined with the direct-path signal after time-delay and phase alignment. Results and Discussions Two simulation scenarios, including jamming only and combined jamming and multipath, are designed to evaluate the proposed method in terms of the output Signal-to-Interference-plus-Noise Ratio (SINR), beam pattern, Bit Error Rate (BER), and Error Vector Magnitude (EVM). Simulation results demonstrate that, under the jamming-only scenario, the proposed method achieves performance close to the theoretical optimum. The output SINR increases with the input Signal-to-Noise Ratio (SNR) at a fixed Jamming-to-Signal Ratio (JSR) (Fig. 3(a)) and remains nearly unchanged as JSR increases at a fixed SNR (Fig. 3(b)), indicating stable jamming suppression capability. The recovered time-domain waveform and spectrum remain highly consistent with the transmitted signal (Fig. 4). The BER curve nearly overlaps that of the optimal beamformer (Fig. 5). At $ {E}_{\rm b}/{N}_{0}=10\;{\mathrm{dB}} $, the recovered Quadrature Phase-Shift Keying (QPSK) constellation closely matches the ideal constellation, achieving an EVM of –11.52 dB (Fig. 6). Under simultaneous jamming and multipath conditions, the proposed framework flexibly suppresses or exploits multipath signals. Compared with multipath suppression, multipath utilization further improves both the output SINR and BER (Fig. 7(a) and Fig. 7(b)). The corresponding beam pattern forms a beam toward the multipath direction rather than a null, demonstrating effective multipath exploitation (Fig. 7(c)). Conclusions This paper proposes a spatial-domain anti-jamming framework for unmanned systems operating under limited prior information. Using only the received mixed signals, the proposed framework estimates the directions of arrival, separates incident signals, and classifies them as direct-path, multipath, or jamming signals. Appropriate suppression or preservation strategies are then applied according to the identified signal type. Therefore, the framework flexibly suppresses or exploits multipath signals while preserving the direct-path signal and mitigating jamming. Simulation results demonstrate the effectiveness of the proposed method in terms of output SINR and demodulation accuracy, confirming reliable jamming suppression and communication performance even when prior information regarding the desired signal, jamming sources, and multipath propagation is unavailable. Future work will investigate the effects of array perturbations, intelligent jamming, and heterogeneous communication modes on the proposed framework and extend it to more complex unmanned-system communication environments.
- Unmanned systems,
- Spatial-domain anti-jamming,
- Adaptive beamforming,
- Spatial smoothing

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References(37)

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