Multistatic Passive Radar for drone detection based Random Finite State

  • Junior Milembolo Miantezila Changchun University of Science and Technology, China
  • Guo Bin Zhongshan Institute of Changchun University of Scince and Technology
  • Wu Jinshuang Intelligent Embedding Technology and Affective Computing Laboratory ,Zhongshan, Guangdong, China
  • Ma Weijiao Intelligent Embedding Technology and Affective Computing Laboratory ,Zhongshan, Guangdong, China
Keywords: Massive MIMO, Passive Radar, UAV, Detection, Spectrum Sensing


Considering the implication of radar sensors in our daily life and environment. Localizing and identifying drones are becoming a research with  a greater focus in recent years.  Consequently, when an unmanned aerial vehicle is used with bad intention, this can lead to a serious public safety and privacy probem. This study investigate pratical use of spectrum range for multistatic passive radar (MSPR) signal processing . Firstly, signal processing is performed after MSPR sensing detection range ,this include multipath energy detection, reference signal extraction, and receiving antenna configuration.  Secondly, based on the MSPR nature, the reference signal is extracted and analyzed. In addition,taking into consideration the vulnerability of passive radar when comes to a moving target localization and real time tracking detection ,a novel method for spectrum sensing and detection which relies on Gaussian filter is proposed. The main goal is to optimize the use of the reference signal extracted with minimum interference as the shared reference signal in spectrum sensing. This will improve the system detection capability and spectrum access. Finally, a recursive method based on Bernoulli random filter is proposed, this takes consideration of drone’s present and unknown states based on time. Moreover, a system is developed meticulously to track and enhance detection of the target. A careful result of the experiment demonstrated that spectral detection can be achieved accurately even when the drone is moving while chasing its position. It shows that Cramer Rao lower error bounds remains significantly within 3% range.


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How to Cite
Milembolo Miantezila, J., Guo Bin, Wu Jinshuang, & Ma Weijiao. (2024). Multistatic Passive Radar for drone detection based Random Finite State. EMITTER International Journal of Engineering Technology, 12(1), 22-47.