A Hybrid Integration and Parameter Estimation Algorithm Based on KTSMF for Sea-Surface Moving Targets Using Space-Based Bistatic Passive Radar.
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| Title: | A Hybrid Integration and Parameter Estimation Algorithm Based on KTSMF for Sea-Surface Moving Targets Using Space-Based Bistatic Passive Radar. |
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| Authors: | Xiang, Jianbing1,2 (AUTHOR), Huang, Lijia1,2 (AUTHOR), Zhong, Lihua1,2,3 (AUTHOR), Zhou, Guangyao1,2 (AUTHOR), Hu, Yuxin1,2,3 (AUTHOR) huyx@aircas.ac.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1479. 25p. |
| Subjects: | Parameter estimation, Bistatic radar, Signal processing, Pulse compression (Signal processing), Doppler effect, Signal-to-noise ratio |
| Abstract: | Highlights: What are the main findings? Proposes KTSMF to effectively mitigate severe range cell migration, Doppler frequency migration, Doppler ambiguity, and spectral aliasing effects under extremely low signal-to-noise ratio conditions. Combines intra-frame coherent integration and inter-frame incoherent integration to realize generalized and robust integration and estimation capability for space-based bistatic sea-surface targets with long-time observation. What are the implications of the main findings? Enables a generalized and robust capability under long-time observation and extremely low signal-to-noise ratio conditions for space-based bistatic sea-surface moving targets that are slow-moving, fast-moving, and highly maneuverable. Achieves a good balance between computational cost and detection performance. A space-based bistatic passive radar system, typically utilizing a satellite as the illuminator of opportunity and ground or aerial platforms as receivers, offers significant advantages for wide-area maritime surveillance, robust anti-jamming performance, and superior survivability. However, due to the limited transmit power and significant path loss over long-range propagation, the signal-to-noise ratio (SNR) of sea-surface targets is extremely low. To achieve effective detection and estimation, long-time integration is required, which can unfortunately induce severe range cell migration (RCM) and Doppler frequency migration (DFM) effects, resulting in integration gain loss and degraded detection performance. This article proposes a hybrid integration and parameter estimation algorithm based on the keystone transform and segmented matched filtering (KTSMF), which partitions the echoes into multiple frames and combines the keystone transform with segmented matched filters for integration. It not only effectively eliminates RCM and DFM effects in both intra-frame and inter-frame processing but also addresses Doppler ambiguity and Doppler aliasing effects, which enables a generalized processing capability for slow-moving, fast-moving, and highly maneuverable targets. Simulation results and analysis demonstrate that the proposed method achieves superior detection performance and parameter estimation accuracy compared to existing algorithms. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Proposes KTSMF to effectively mitigate severe range cell migration, Doppler frequency migration, Doppler ambiguity, and spectral aliasing effects under extremely low signal-to-noise ratio conditions. Combines intra-frame coherent integration and inter-frame incoherent integration to realize generalized and robust integration and estimation capability for space-based bistatic sea-surface targets with long-time observation. What are the implications of the main findings? Enables a generalized and robust capability under long-time observation and extremely low signal-to-noise ratio conditions for space-based bistatic sea-surface moving targets that are slow-moving, fast-moving, and highly maneuverable. Achieves a good balance between computational cost and detection performance. A space-based bistatic passive radar system, typically utilizing a satellite as the illuminator of opportunity and ground or aerial platforms as receivers, offers significant advantages for wide-area maritime surveillance, robust anti-jamming performance, and superior survivability. However, due to the limited transmit power and significant path loss over long-range propagation, the signal-to-noise ratio (SNR) of sea-surface targets is extremely low. To achieve effective detection and estimation, long-time integration is required, which can unfortunately induce severe range cell migration (RCM) and Doppler frequency migration (DFM) effects, resulting in integration gain loss and degraded detection performance. This article proposes a hybrid integration and parameter estimation algorithm based on the keystone transform and segmented matched filtering (KTSMF), which partitions the echoes into multiple frames and combines the keystone transform with segmented matched filters for integration. It not only effectively eliminates RCM and DFM effects in both intra-frame and inter-frame processing but also addresses Doppler ambiguity and Doppler aliasing effects, which enables a generalized processing capability for slow-moving, fast-moving, and highly maneuverable targets. Simulation results and analysis demonstrate that the proposed method achieves superior detection performance and parameter estimation accuracy compared to existing algorithms. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101479 |
