Resolution probability is the most important indicator for signal parameter estimator, including estimating time delay, and joint Doppler shift and time delay. In order to get high-resolution probability, some procedures have been suggested such as compressed sensing. Based on the signal’s sparsity, compressed sensing has been used to estimate signal parameters in recent research. After solving ℓ₀ norm Optimization problem, the methods would achieve high resolution. These methods all require high SNR. In order to improve the performance in low SNR, a novel implementation is proposed in this paper. We give a sparsity representation for the generalized matched filter output, or ambiguity function, while the former methods utilized the sparsity representation for channel response in time domain. By deconvolving the generalized matched filter output, 2-dimension estimation for Doppler shift and time delay would be gotten by greedy method, optimization method based on relaxation, or Bayesian method. Simulation demonstrates our method has better performance in low SNR than the method by the channel sparsity representation.

Real-time Doppler estimation of sonar signals is of great significance to underwater communication and detection. The Hyperbolic-Frequency-Modulated (HFM) waveform-based speed spectrum estimation is one of the modern methods for underwater Doppler estimation, and this paper presents a detailed analyses of the derivation and the performance of this method. The analyzing results show the robustness of the method on multipath channels and multi-highlight targets and provide the main lobe width of the speed spectrum. Moreover, the paper examines the spectral properties of the HFM signal from the frequency-domain Doppler invariance property, which could provide insights for better understanding and utilization of this Doppler invariant waveform.

Underwater acoustic positioning using ultrashort baseline (USBL) technology is essential for underwater navigation and ocean surveillance. Many research institutions and commercial organizations have conducted extensive studies on USBL, with wideband processing widely applied to enhance the capabilities of related methods. However, the properties of the wideband correlation spectrum have not been thoroughly explored. This study introduces a linear fitting technique, leveraging the frequency-phase relationship within the spectrum to enhance the USBL performance. By combining iterative reweighted least squares and a random sampling and consensus (RANSAC)-type approach, the method reduces the impact of outliers, particularly in multiple-path and low signal-to-noise ratio (SNR) scenarios. Furthermore, we operate a matched filter before the linear fitting process and suggest rescreening based on the consistency of all array elements in the frequency domain to eliminate more outliers. The proposed method significantly reduces positioning errors in both numerical simulations and sea trial data processing.