The deployment of the low earth orbit(LEO)satellites provides a large number of signals of opportunity(SOPs),unmanned aerial vehicle(UAV)positioning and navigation via LEO-SOPs have re-ceived much attention.Current re...The deployment of the low earth orbit(LEO)satellites provides a large number of signals of opportunity(SOPs),unmanned aerial vehicle(UAV)positioning and navigation via LEO-SOPs have re-ceived much attention.Current research is focused on Doppler positioning techniques,which require the collaboration of multiple satellites(≥3).However,the dynamic changes of LEO satellites weaken the generalization ability of Doppler positioning.In this paper,a direct position determination(DPD)method with uniform circular array(UCA)is proposed for UAV positioning from the perspective of the spatial spectrum estimation of LEO-SOPs.The proposed method employs the orthogonality between the signal and noise subspaces of the covariance matrix of the different received SOPs to establish the cost function for UAV’s coordinate.Instead of the multiple dimen-sional search,a root mean square propagation(RM-SProp)gradient optimizer with an adaptive learning rate is developed to find the coordinate of UAV.The effectiveness and robustness of the proposed method are verified using numerical data generated from the systems tool kit(STK).展开更多
The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference...The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference of Arrival （FDOA）, do not perform well under low Signal-to-Noise Ratio （SNR）, and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound （CRLB）. The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.展开更多
This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit(LEO) satellites. As the high signal-to-noise ratio(SNR) reception cannot always be guarante...This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit(LEO) satellites. As the high signal-to-noise ratio(SNR) reception cannot always be guaranteed at LEO satellites, the recently developed direct position determination(DPD)is adopted. For LEO satellite-based passive localization systems, an efficient DPD is challenging due to the excessive exhaustive search range leading from broad satellite coverage. In order to reduce the computational complexity, we propose a time difference of arrival-assisted DPD(TA-DPD) which minimizes the searching area by the time difference of arrival measurements and their variances. In this way, the size of the searching area is determined by both geometrical constraints and qualities of received signals, and signals with higher SNRs can be positioned more efficiently as their searching areas are generally smaller.Both two-dimensional and three-dimensional passive localization simulations using the proposed TA-DPD are provided to demonstrate its efficiency and validity. The superior accuracy performance of the proposed method, especially at low SNRs conditions, is also verified through the comparison to conventional two-step methods. Providing a larger margin in link budget for satellite-based vessel location acquisition,the TA-DPD can be a competitive candidate for trusted marine location service.展开更多
The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute th...The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.展开更多
基金supported by Jiangsu Province Fron-tier Leading Technology Basic Research Project(BK20222013)partly supported by National Natural Science Foundation of China(No.62101251)+1 种基金Fundamental Research Funds for the Central Universities(No.NS20240240)partly supported by Post-graduate Research&Practice Innovation Program of NUAA(No.016001)。
文摘The deployment of the low earth orbit(LEO)satellites provides a large number of signals of opportunity(SOPs),unmanned aerial vehicle(UAV)positioning and navigation via LEO-SOPs have re-ceived much attention.Current research is focused on Doppler positioning techniques,which require the collaboration of multiple satellites(≥3).However,the dynamic changes of LEO satellites weaken the generalization ability of Doppler positioning.In this paper,a direct position determination(DPD)method with uniform circular array(UCA)is proposed for UAV positioning from the perspective of the spatial spectrum estimation of LEO-SOPs.The proposed method employs the orthogonality between the signal and noise subspaces of the covariance matrix of the different received SOPs to establish the cost function for UAV’s coordinate.Instead of the multiple dimen-sional search,a root mean square propagation(RM-SProp)gradient optimizer with an adaptive learning rate is developed to find the coordinate of UAV.The effectiveness and robustness of the proposed method are verified using numerical data generated from the systems tool kit(STK).
基金supported by the National Natural Science Foundation of China(No.61401513)
文摘The localization of a stationary transmitter using moving receivers is considered. The original Direct Position Determination （DPD） methods, with combined Time Difference of Arrival （TDOA） and Frequency Difference of Arrival （FDOA）, do not perform well under low Signal-to-Noise Ratio （SNR）, and worse still, the computation cost is difficult to accept when the computational capabilities are limited. To get better positioning performance, we present a new DPD algorithm that proves to be more computationally efficient and more precise for weak signals than the conventional approach. The algorithm partitions the signal received with the same receiver into multiple non-overlapping short-time signal segments, and then uses the TDOA, the FDOA and the coherency among the short-time signals to locate the target. The fast maximum likelihood estimation, one iterative method based on particle filter, is designed to solve the problem of high computation load. A secondary but important result is a derivation of closed-form expressions of the Cramer-Rao Lower Bound （CRLB）. The simulation results show that the algorithm proposed in this paper outperforms the traditional DPD algorithms with more accurate results and higher computational efficiency, and especially at low SNR, it is more close to the CRLB.
基金supported in part by the National Key Research and Development Program of China under Grant No. 2019YFB1803200the National Natural Science Foundation of China (NSFC) under Grant No. 61901020the Civil Aviation Administration of China。
文摘This paper focuses on the trusted vessel position acquisition using passive localization based on the booming low-earth-orbit(LEO) satellites. As the high signal-to-noise ratio(SNR) reception cannot always be guaranteed at LEO satellites, the recently developed direct position determination(DPD)is adopted. For LEO satellite-based passive localization systems, an efficient DPD is challenging due to the excessive exhaustive search range leading from broad satellite coverage. In order to reduce the computational complexity, we propose a time difference of arrival-assisted DPD(TA-DPD) which minimizes the searching area by the time difference of arrival measurements and their variances. In this way, the size of the searching area is determined by both geometrical constraints and qualities of received signals, and signals with higher SNRs can be positioned more efficiently as their searching areas are generally smaller.Both two-dimensional and three-dimensional passive localization simulations using the proposed TA-DPD are provided to demonstrate its efficiency and validity. The superior accuracy performance of the proposed method, especially at low SNRs conditions, is also verified through the comparison to conventional two-step methods. Providing a larger margin in link budget for satellite-based vessel location acquisition,the TA-DPD can be a competitive candidate for trusted marine location service.
基金supported by the National Natural Science Foundation of China(61101173)
文摘The conventional direct position determination（DPD） algorithm processes all received signals on a single sensor.When sensors have limited computational capabilities or energy storage,it is desirable to distribute the computation among other sensors.A distributed adaptive DPD（DADPD）algorithm based on diffusion framework is proposed for emitter localization.Unlike the corresponding centralized adaptive DPD（CADPD） algorithm,all but one sensor in the proposed algorithm participate in processing the received signals and estimating the common emitter position,respectively.The computational load and energy consumption on a single sensor in the CADPD algorithm is distributed among other computing sensors in a balanced manner.Exactly the same iterative localization algorithm is carried out in each computing sensor,respectively,and the algorithm in each computing sensor exhibits quite similar convergence behavior.The difference of the localization and tracking performance between the proposed distributed algorithm and the corresponding CADPD algorithm is negligible through simulation evaluations.
