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Volume 46 Issue 10
Oct.  2024
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DENG Honggao, YU Runhua, JI Yuanfa, WU Sunyong, SUN Xiyan. Mobile Radar Registration with Multiple Targets Based on Bernoulli Filter[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4035-4043. doi: 10.11999/JEIT240013
Citation: DENG Honggao, YU Runhua, JI Yuanfa, WU Sunyong, SUN Xiyan. Mobile Radar Registration with Multiple Targets Based on Bernoulli Filter[J]. Journal of Electronics & Information Technology, 2024, 46(10): 4035-4043. doi: 10.11999/JEIT240013

Mobile Radar Registration with Multiple Targets Based on Bernoulli Filter

doi: 10.11999/JEIT240013
Funds:  The National Natural Science Foundation of China (U23A20280, 62061010, 62161007), Guangxi Science and Technology Department Project (AB23026120)
  • Received Date: 2024-01-16
  • Rev Recd Date: 2024-09-05
  • Available Online: 2024-09-11
  • Publish Date: 2024-10-30
  • Traditional methods for multi-target bias registration in networked radar system typically assume that the data association relationship is known. However, in the case of platform maneuvering, there are simultaneously radar measurement biases and platform attitude angle biases, and the radar observation process is prone to clutter interference, resulting in difficulties in data association. To address this issue, a multi-target mobile radar bias registration method based on Bernoulli filter is proposed. Firstly, the measurement and state equations for the system biases are established, and then the system biases are modeled as a Bernoulli random finite set. The recursive estimation of the system biases under the Bernoulli filtering framework is achieved using the original measurements in a common coordinate system, effectively avoiding the data association. Additionally, to fully utilize multi-target measurement information, a modified greedy measurement partitioning method is proposed to select the optimal measurement subset corresponding to the system biases at each filtering time step, and the centralized fusion estimation of the system biases is performed using the multi-measurement information in the measurement subset, improving the estimation accuracy and convergence speed of the system biases. Simulation experiments show that the proposed method can effectively estimate radar measurement biases and platform attitude angle biases in multi-target and cluttered scenarios with unknown data association. Moreover, this method demonstrates strong adaptability when the platform attitude angle variation rate is low.
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