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| Citation: | LIU Pingyu, LYU Xiaode, LIU Zhongsheng, et al. Research on co-channel interference suppression method for passive radar based on the jiont processing of primary and reference channels[J]. Journal of Radars, 2020, 9(6): 974–986. doi: 10.12000/JR19047
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Research on Co-channel Interference Suppression Method for Passive Radar Based on the Jiont Processing of Primary and Reference Channels
doi: 10.12000/JR19047
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Abstract
The illuminators of passive radar based civil communication signals are densely distributed. As a result, the co-channel illuminator always interferes with the primary and reference channels, resulting in poor detection performance. To solve the aforementioned problem, an improved signal processing flow with co-channel interference suppression is proposed in this paper. First, signals from all channels were processed jointly. The direct-path wave of each illuminator was estimated using the multi-channel blind deconvolution algorithm. Then, the direct-path wave of the primary illuminator was identified as the reference signal by applying the difference in the proportion of the primary illuminator signal energy among channels. Then, the clutter of each illuminator in the primary channel was suppressed by utilizing each of the above estimated signals. Finally, the residual signal, after cancellation, was used to compute the cross-ambiguity functions with the identified direct-path wave of the primary illuminator for target detection. The improved flow can promote the cancellation ratio and reduce the bottom noise of the cross-ambiguity function and missed alarm. Co-channel interference can be effectively suppressed using the improved processing flow without changing the radar system’s hardware. The validity of the proposed method were confirmed by the results of the simulation and experiment. -
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References
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- Figure 1. Signal processing flows of passive radar
- Figure 2. Schematic diagram of multi-channel blind deconvolution algorithm
- Figure 3.
- Figure 4. Processing results (range-Doppler plane of the cross-ambiguity function)
- Figure 5. Processing result (range profile of the cross-ambiguity function)
- Figure 6. Experiment scenes
- Figure 7. Processing result (3-D graph of the cross-ambiguity function)
- Figure 8. Processing result (range profile of the cross-ambiguity function)