Journal
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Volume 70, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2020.3045193
Keywords
Bearing angle of arrival (BAOA); bearing; Cramer-Rao lower bound (CRLB); iterative constrained weighted least squares; time difference of arrival (TDOA); ultrashort baseline (USBL)
Funding
- National Natural Science Foundation of China [52071080]
- Inertial Technology Key Lab Fund [614250607011709]
- Fundamental Research Funds for the Central Universities [2242020k1G009]
- Key Laboratory Fund for Underwater Information and Control [614221805051809]
- Jiangsu Key Laboratory Fund for Green Ship Technology [2019Z01]
- National Natural Science Foundation of Southeast University [9S20172204]
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This article addresses the underwater acoustical localization problem using TDOA and BAOA measurements, developing a closed-form solution for the hybrid TDOA/BAOA-based source localization problem. An iterative constrained weighted least-squares method is presented to minimize error in the case of large noise. The solution is proven to achieve the CRLB performance, outperforming traditional methods in estimation bias and accuracy, achieving better CRIB performance.
This article addresses the underwater acoustical localization problem by using the time-difference-of-arrival (TDOA) and bearing-angle-of-arrival (BAOA) measurements. For the underwater acoustic equipment, such as the ultrashort baseline system (USW, whose hearing measurements are different from the traditional angle-of-arrival (AOA) model, a closed-form solution for the hybrid TDOA/BAOA-based source localization problem is developed. However, the solution suffers from the measurement noise and cannot achieve the Cramer-Rao lower bound (CRLB) performance in the case of large measurement noise. Thus, an iterative constrained weighted least-squares method is presented to further minimize the error in the case of large noise. The CRLB for hybrid TDOA/BAOA source localization is analyzed, and the solution is proved to achieve the CRLB performance. Numerical simulations and field tests demonstrate that the proposed method outperforms the traditional methods in terms of estimation bias and accuracy. It can achieve the CRIB performance better.
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