Aiming at the challenge of accurate diving modeling of a smart float, an anti-saturation and noise least squares (ASNLS) algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction.

Firstly, the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model, and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling. Subsequently, the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise. Finally, by adjusting the values of the covariance matrix, the designed diving parameter identification algorithm achieved resistance to data saturation.

Based on the data of the South China Sea deep diving experiment of the smart float in 2021, diving model parameter identification and depth prediction are carried out. The results demonstrate that the ASNLS algorithm has faster convergence speed (31.8% higher than the least squares algorithm) and smaller depth prediction error (average absolute percentage errors less than 9% at different depths) than both the traditional least squares algorithm and supports the vector machine algorithm.

Consequently, the ASNLS algorithm can provide effective support for the depth control and prediction of the smart float.