Conventional cross-correlation (CC) and matched filter (MF) algorithms fail to resolve the multipath signals in cases when they are spaced within a Rayleigh resolution limit. Further post-processing of the CC outputs is necessary to improve the performance of CC. In this work, we propose two high-resolution time delay estimation algorithms by post-processing of the CC outputs. The first method applies a Richardson-Lucy deconvolution algorithm used in image deblurring to the CC outputs (Dec-CC). The proposed Dec-CC algorithm yields narrow beams to improve the estimation resolution and accuracy and achieve excellent performance in low signal-to-noise ratio (SNR) environments. The second method reformulates CC as a sparse signal recovery (SSR) problem and estimates time delay via weighted L1 norm minimization (CC-WL1). CC-WL1 also performs excellently for low SNR cases. Simulation results show that the proposed Dec-CC and CC-WL1 algorithms outperform other counterparts.

High-accuracy real-time Doppler estimation is one of the key problems in underwater acoustic communication systems. Using the dual-hyperbolic-frequency-modulation (dual-HFM) synchronous signal is an effective solution; however, the Doppler estimation accuracy is limited by the sampling frequency in the traditional methods. In this letter, we propose a high-accuracy speed spectrum scanning method based on the Doppler invariant and the frequency spectrum properties of the HFM signal. This signal processing system uses dual-HFM waveform as a synchronous signal, constructs the speed spectrum function at the receiver, and then obtains the Doppler estimate through one-dimensional scanning. This method breaks through the speed resolution limit induced by the sampling frequency in the traditional method and obtains a high-accuracy estimate. The influences of signal transmission and processing parameters on the Doppler estimation is analyzed, and the parameter selection criteria is presented to improve the reliability of the estimation. The results of both numerical simulation and underwater communication experiments prove the effectiveness of the proposed method.

A unitary transformation-based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) estimation of coherent signals. The problem is solved by reorganizing the covariance matrix into a block Hankel one for decorrelation first and then reconstructing a new matrix to facilitate the unitary transformation. By multiplying unitary matrices, eigenvalue decomposition and singular value decomposition are both transformed into real-valued, so that the computational complexity can be reduced significantly. In addition, a fast and computationally attractive realization of the 2-D unitary transformation is given by making a Kronecker product of the 1-D matrices. Compared with the existing 2-D algorithms, our scheme is more efficient in computation and less restrictive on the array geometry. The processing of the received data matrix before unitary transformation combines the estimation of signal parameters via rotational invariance techniques (ESPRIT)-Like method and the forward-backward averaging, which can decorrelate the impinging signalsmore thoroughly. Simulation results and computational order analysis are presented to verify the validity and effectiveness of the proposed algorithm.

Detecting low‐speed quiet targets such as divers and underwater‐unmanned‐vehicles in littoral environment by using active sonar is becoming increasingly attractive. When using a Doppler insensitive pulsed linearly frequency modulated signal, the high‐level clutters which might arise from underwater physical scatterers will lead to excessive false alarm rates and limit the detection performance. However, Doppler sensitive waveforms such as binary phase shift keying have the capability of filtering clutters and degrading false alarms. Thus, the question how to estimate the clutter‐suppressing performance of waveforms is essential for sonar system design. In this study, the clutter‐rejecting principle of waveform is theoretically introduced firstly. Then, based on the reverberation statistical features, this study proposes two methods, the Doppler‐statistic method and envelope‐statistic method, separately, to estimate and evaluate the clutter suppressing performance of waveforms. Finally, the methods are verified by lake experiments. It is proved that the first method has the capacity of calculating the confidence probability of suppressing clutters by a waveform, and through the second method, the clutter rejecting performance of waveforms can be evaluated and verified. The methods can be used for selecting and designing waveforms to reduce false alarms and improve detection performance.

The Doppler shift of low-speed targets is frequently disturbed by the reverberation Doppler spread clutter under the shallow sea. The clutter is generated by underwater scatterers, which increases the difficulty of Doppler estimation. To solve this problem, a reverberation target resolution function based on the Doppler spread clutter statistical model is proposed in this paper. Through the width of reverberation Doppler clutter, this function adjusts the waveform parameters by determining whether the target is discriminable. In addition, the reverberation Doppler spread clutter is time-spatial varying and affected by grazing angle, waves, wind speed, fish and other effects. Thus, the sonar waveform parameters need to be adjusted constantly. Therefore, this paper combines the cognitive sonar based on reinforcement learning with the reverberation target resolution function to evaluate different waveforms in different environments. Consequently, the sonar can adjust the waveform parameters in real-time and obtain the optimal waveform in different environments. Meanwhile, in this paper, the action selection strategy of Dyna-Q reinforcement learning is optimized, and the model-based maximum action selection Dyna-Q algorithm (Dyna-Q-Max-Action) is proposed. Compared with the traditional Dyna-Q and Q-learning algorithms, the proposed algorithm needs fewer episodes. Finally, numerical simulation verified the effectiveness of the proposed algorithm.