For the problem that the existing methods for noise type recognition and intensity estimation all focus on single noises, and cannot estimate the intensity of source noises in the mixed noises, a K-Nearest Neighbors ( KNN) algorithm with distance threshold was proposed to recognize the single and mixed noises, and estimate the intensity of source noises in the mixed noises by combining the recognition results of mixed noises and the reconstruction of noise bases. Firstly, the data distribution in frequency domain was used as feature vector. Then the signals were identified by the noise type recognition algorithm, and the frequency domain cosine distance between reconstructed noise and real noise was adopted as the optimal evaluation criterion in the process of reconstruction of noise bases. Finally, the intensity of source noises was estimated. The experimental results on two test databases indicate that, the proposed algorithm has the average accuracy of noise type identification as high as 98.135%, and the error rate of intensity estimation of mixed noise of 20.96%. The results verify the accuracy and generalization of noise type recognition algorithm as well as the feasibility of mixed noise intensity estimation algorithm, and this method provides a new idea for the mixed noise intensity estimation. The information of mixed noise type and intensity obtained by this method contributes to the determination of denoising methods and parameters, and improves the denoising efficiency.

In underwater acoustic communication, multipath effect channel can cause severe Inter-Symbol Interference (ISI). In view of the problems of the existing equalization algorithms when dealing with ISI, including slow convergence speed and huge steady-state error, as well as the complicated algorithm and being difficult to carry out hardware migration, a new variable step Least Mean Square (LMS) algorithm was proposed with anticosine step function and three adjustment parameters within the Feed-Forward Equalizer and Decision Feed-back Equalizer (FFE-DFE) structure. Firstly, simulations of three adjustment parameters including α, β, r were given to optimize the algorithm and compare it with traditional LMS algorithm, Modified Arctangent based Variable Step LMS (MA-VSLMS) and Hyperbolic Secant function based Variable Step size LMS algorithm (HS-VSLMS) in convergence and steady-state error. The simulation results showed that compared with the traditional LMS algorithm, the convergence speed of the proposed algorithm was 57.9% higher, and the steady-state error was reduced by 2 dB; compared with HS-VSLMS and MA-VSLMS, the convergence speed of the proposed algorithm was 26.3% and 15.8% higher, respectively, and the steady-state error was reduced by 1-2 dB. Finally, the proposed algorithm was transplanted to signal processing module and tested in an underwater experiment. Experimental results indicate that the signal is recovered very well after the equalizer, and the ISI problem caused by multipath effect is solved in the actual scene.