This paper introduces an iterative technique aimed at jointly estimating target location and sound propagation speed in a 3D underwater environment characterized by an isogradient sound speed profile. The developed algorithm is based on the Modified Polar Representation framework and offers a unified solution for underwater near and far-field target localization. This solution enables precise identification of the source position in the near-field and estimation of the direction of arrival in the far-field. Prior MPR-based solutions typically assume sound propagation along a straight path at a known, constant speed within the medium, an assumption which fails in complex underwater environments, resulting in excessive distance estimation bias and thus impacting positioning accuracy. Our paper resolves this concern by introducing compensation for the stratification effect of the SSP. The proposed algorithm firstly employs a constrained weighted least squares approach, incorporating fixed weight matrices and constraint terms to generate an initial solution. Subsequently, through stratification compensation and iterative refinement, the accuracy of source localization is enhanced. Extensive simulations, conducted using BELLHOP, demonstrate that the positioning algorithm can attain CRLB accuracy under moderate noise levels. Its superior performance in challenging underwater environments is further validated through comparative analysis with several existing algorithms.

Underwater acoustic localization (UWAL) is extremely challenging due to the multipath nature of extreme underwater environments, the sensor position uncertainty caused by unpredictable ocean currents, and the lack of underwater observation data due to sparse array, which all affect localization performance. Addressing these issues, this paper proposes a simple and effective underwater acoustic localization method using the time difference of arrival (TDOA) measurements based on the multipath channel effect of the underwater environment. By introducing the calibration source, localization performance was improved, and the sensor position error was corrected. The Cramér–Rao lower bound (CRLB) was derived, and the proposed method was able to achieve the CRLB with small deviation. Numerical simulations confirm the improved performance of the proposed method, including (1) a 20 dB and 30 dB reduction in the CRLB for far and near source scenarios, respectively, indicating improved accuracy and reliability when estimating unknown sources; (2) better Mean Squared Error (MSE) performance compared to existing methods and an efficiency of over 90% in low noise and above 80% in moderate noise in several scenarios, with a delayed threshold effect; and (3) achieving CRLB performance with only three sensors in a 3D space, even under moderate noise, while existing methods require at least five sensors for comparable performance. Our results demonstrate the efficacy of the proposed method in enhancing the accuracy and efficiency of source localization.

Source localization in underwater sensor networks (UWSNs) presents complex challenges due to sensor nodes drift caused by ocean currents, non-line-of-sight (NLOS) propagation resulting from underwater multipath effects, and environmental noise. This paper proposes a practical and innovative algebraic solution based on the time difference of arrival (TDOA) for source localization in shallow seas. The proposed solution effectively addresses the issues arising from sensor position errors and multipath effects by incorporating the sea-surface reflection non-line-of-sight (SNLOS) link and optimizing the algorithm, thereby significantly improving positioning accuracy. The core concept of the method involves utilizing the weighted least squares algorithm to obtain an initial estimate of the source position, followed by direct estimation of the bias and subsequent refinement of the solution. In contrast to traditional closed-form solutions, this method avoids the introduction of intermediate parameters and directly handles the estimated bias from the previous step. Even when only considering the line-of-sight (LOS) link, the proposed solution achieves precise localization with a minimal number of sensors. Theoretical analysis demonstrates that the solution can achieve the Cramér–Rao lower bound (CRLB) accuracy under low noise conditions, and simulation results validate the superior performance of the proposed solution.

This study proposes a stepwise accuracy enhancement (SAE) method for jointly estimating signal propagation speed, near-field position, and far-field direction-of-arrival (DOA) without requiring prior source range knowledge. Time difference of arrival (TDOA) methods excel at near-field localization but are limited in the far field by the thresholding effect. The modified polar representation (MPR) framework mitigates this limitation by unifying near and far field localization. However, previous MPR-based studies assumed known propagation speed, often impractical. SAE overcomes this by treating propagation speed as an unknown parameter to be concurrently estimated with source location. SAE formulates this joint estimation as a quadratically constrained quadratic optimization (QCQP) problem, then mitigates the parameter estimation bias by minimizing the impact of TDOA measurement errors. Simulations validate the effectiveness of SAE for joint estimation irrespective of source range, achieving the Cramér-Rao lower bound (CRLB) in small noise.

The diurnal fluctuation of acoustic propagation in a cold dome region was studied by using the output of the high-resolution Finite Volume Coastal Ocean Model. The 3-D acoustic propagation in this region was studied first, and the results suggest that the diurnal fluctuation of the ocean environment results in the fluctuation of acoustic propagation. However, the variation pattern cannot be determined with only few outputs, and comprehensive analysis was needed. Then, a feature model for the thermal front on the edge of the cold dome was proposed. By analyzing the output of the ocean model, fluctuations were found to mainly occur in the two aspects of the feature model: first, horizontal excursion of the thermal front and second, vertical motion of the isothermal lines in the cold dome. The variation pattern of acoustic propagation over the two kinds of fluctuations was quantified. Results suggest that both kinds of fluctuations of model parameters result in great transmission loss (TL) variation in some cases. Typically, acoustic propagation with high frequency and deep source experience great influence from the fluctuations. The TL versus range usually experience influence from the fluctuations of the first kind from an approximately 40-km range and from the start for the second kind. The reason is that the vertical motion of the isothermal lines of the cold dome induces variation over the temperature profile from the start until the end, whereas the horizontal excursion of the thermal front brings influence mainly over the temperature profiles near the thermal front.