TY - JOUR
T1 - Beidou-based passive radar vessel target detection
T2 - Method and experiment via long-time optimized integration
AU - Huang, Chuan
AU - Li, Zhongyu
AU - Lou, Mingyue
AU - Qiu, Xingye
AU - An, Hongyang
AU - Wu, Junjie
AU - Yang, Jianyu
AU - Huang, Wei
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method to obtain an adequate signal-to-noise ratio (SNR). During the long observation time, the range migration, intricate Doppler migration, and noncoherence charac-teristic bring challenges to the integration processing. In this paper, first, the keystone transform is applied to correct the range walk. Then, considering the noncoherence of the entire echo, the hybrid integration strategy is adopted. To remove the Doppler migration and correct the residual range migration, the long-time integration is modeled as an optimization problem. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which the target echo over the long observation time is well concentrated, providing a reliable detection performance for the BeiDou-based passive radar. Its effectiveness is shown by the simulated and experimental results.
AB - The BeiDou navigation satellite system shows its potential for passive radar vessel target detection owing to its global-scale coverage. However, the restrained power budget from BeiDou satellite hampers the detection performance. To solve this limitation, this paper proposes a long-time optimized integration method to obtain an adequate signal-to-noise ratio (SNR). During the long observation time, the range migration, intricate Doppler migration, and noncoherence charac-teristic bring challenges to the integration processing. In this paper, first, the keystone transform is applied to correct the range walk. Then, considering the noncoherence of the entire echo, the hybrid integration strategy is adopted. To remove the Doppler migration and correct the residual range migration, the long-time integration is modeled as an optimization problem. Finally, the particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which the target echo over the long observation time is well concentrated, providing a reliable detection performance for the BeiDou-based passive radar. Its effectiveness is shown by the simulated and experimental results.
KW - BeiDou-based passive radar
KW - Long-time integration
KW - Maritime surveillance
KW - Vessel target detection
UR - https://www.scopus.com/pages/publications/85116263964
U2 - 10.3390/rs13193933
DO - 10.3390/rs13193933
M3 - 文章
AN - SCOPUS:85116263964
SN - 2072-4292
VL - 13
JO - Remote Sensing
JF - Remote Sensing
IS - 19
M1 - 3933
ER -