YU Hua, OU Yuanyuan, CHEN Yonghua, XU Lijun. A Fast Localization Algorithm for Underwater Targets Using Joint One-Dimensional Angle of Arrival and Time Difference of Arrival[J]. JOURNAL OF SIGNAL PROCESSING, 2023, 39(10): 1857-1865. DOI: 10.16798/j.issn.1003-0530.2023.10.012
Citation: YU Hua, OU Yuanyuan, CHEN Yonghua, XU Lijun. A Fast Localization Algorithm for Underwater Targets Using Joint One-Dimensional Angle of Arrival and Time Difference of Arrival[J]. JOURNAL OF SIGNAL PROCESSING, 2023, 39(10): 1857-1865. DOI: 10.16798/j.issn.1003-0530.2023.10.012

A Fast Localization Algorithm for Underwater Targets Using Joint One-Dimensional Angle of Arrival and Time Difference of Arrival

  • ‍ ‍To address the issues of low positioning accuracy due to underwater current drift or engineering measurement-induced sensor location errors and the use of only a single observation parameter, this paper proposes a fast underwater target positioning algorithm that jointly utilizes one-dimensional angle of arrival (1-D AOA) and time difference of arrival (TDOA), which determines the target position by the intersection point of a cone and a hyperboloid. First, under the influence of observation noise and linear array midpoint position perturbation noise, we derived the nonlinear equation relating the joint 1-D AOA and TDOA observations to the target position. Next, we proposed a two-step weighted least squares (WLS) solving algorithm. In the first step, the algorithm introduces auxiliary variables to transform the nonlinear equation into a pseudo-linear equation and uses the WLS method to obtain a rough estimate of the target position. In the second step, the relationship between the target position and auxiliary variables is used to construct a new equation, and the WLS method is applied again to obtain a more accurate target position estimate. Subsequently, we derived the Cramer-Rao lower bound (CRLB) under observation noise and linear array midpoint position perturbation noise to evaluate positioning performance. Simulation results show that, compared to existing algorithms that jointly use 1-D AOA and TDOA measurements, the proposed algorithm considers linear array midpoint position errors and achieves higher positioning accuracy in scenarios with sensor location errors.
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