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.

Integrated sensing and communication (ISAC) waveform design has emerged as a promising technique. However, the existing power-domain design techniques involve complex operations, and are difficult to strike a balance between sensing and communication performance. In this letter, we propose a power-domain non-orthogonal ISAC waveform design. Our approach involves the quantization of the sensing symbol and its subsequent combination with the communication symbol at the transmitters. This process enables successful separation of communication symbols at the user end while maintaining an acceptable level of sensing performance compared to unquantized sensing symbols. Additionally, the quantization order and power allocation index can be adjusted to flexibly balance the bit error rate and estimation error in delay and Doppler. Simulation results validate the effectiveness of the proposed ISAC waveform design.

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.