Related Experiment Video
Updated: Aug 5, 2026
09:32
Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
Published on: November 20, 2017
Robust Sparse Underwater Acoustic Channel Estimation Using a Bidirectional Proportionate Recursive Maximum
Xiao-Chen Chen1, Guan-Quan Dai1,2, Yang Shi1
1College of Navigation, Jimei University, Xiamen 361021, China.
Entropy (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a new algorithm for underwater acoustic communication channel estimation, improving accuracy and robustness against noise and channel changes. The bidirectional proportionate recursive maximum correntropy criterion (Bi-PRMCC) algorithm enhances sparse channel estimation.
Area of Science:
- Underwater Acoustic Communication
- Signal Processing
- Channel Estimation
Background:
- Sparse channel estimation in underwater acoustic communication faces challenges from multipath propagation, non-Gaussian noise, and time-varying channels.
- Existing algorithms struggle with robustness and accuracy in complex underwater environments.
- Accurate channel estimation is crucial for reliable underwater acoustic communication systems.
Purpose of the Study:
- To propose a novel bidirectional proportionate recursive maximum correntropy criterion (Bi-PRMCC) algorithm for enhanced sparse channel estimation in underwater acoustic communication.
- To improve the accuracy, robustness, and tracking capability of channel estimation under complex conditions.
- To address the limitations of existing algorithms in handling non-Gaussian noise and abrupt channel variations.
Main Methods:
- Developed a bidirectional filtering structure integrated with the proportionate recursive maximum correntropy criterion (PRMCC) framework.
- Utilized the maximum correntropy criterion for enhanced robustness against non-Gaussian impulsive noise.
- Employed a proportionate update mechanism for improved identification of dominant sparse channel taps.
- Simulated underwater acoustic channels using the Bellhop ray-tracing model under various non-Gaussian noise conditions (Cauchy, α-stable, Middleton).
Main Results:
- The Bi-PRMCC algorithm demonstrated lower steady-state normalized mean square deviation (NMSD) compared to RLS, Bi-RLS, PRLS, RMCC, and PRMCC under non-Gaussian noise.
- The proposed algorithm exhibited superior robustness against impulsive noise and maintained high estimation performance across different signal-to-noise ratios.
- Bi-PRMCC showed rapid reconvergence after abrupt channel variations, indicating favorable tracking capabilities.
- Ablation studies confirmed performance gains from the bidirectional structure.
Conclusions:
- The Bi-PRMCC algorithm offers significant improvements in estimation accuracy and robustness for sparse underwater acoustic channels.
- It effectively mitigates the impact of complex multipath propagation and non-Gaussian impulsive noise.
- The algorithm's ability to track abrupt channel variations makes it suitable for dynamic underwater environments.
- Bi-PRMCC represents a robust and accurate solution for challenging underwater acoustic communication scenarios.
Related Concept Videos
01:26Linear Approximation in Frequency Domain
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
01:21Linear Approximation in Time Domain
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
01:11Residuals and Least-Squares Property
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
01:08Determination of Expected Frequency
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
01:05Estimation of the Physical Quantities
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
01:06Sampling Methods: Overview
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling.
In analytical chemistry, the choice of sampling...
In analytical chemistry, the choice of sampling...