带基准点的低通信开销扩散最小总误差熵算法OA

In complex environments with variable errors and non-Gaussian interference,traditional diffusion-based adap-tive filtering algorithms based on second-order statistics suffer from estimation bias and are highly sensitive to outliers and heavy-tailed noise,leading to a significant deterioration in distributed network performance.Accordingly,this study proposes an information-theoretic robust filtering method,i.e.,a diffusion minimum total error entropy with fidu-cial points(DMTEF)algorithm.This algorithm introduces the minimum error entropy criterion with fiducial points into the diffusion network,thereby significantly improving its robustness under non-Gaussian noise.To adapt to resource-constrained distributed networks,this study further proposes an improved method with low communication overhead,i.e.,a partial-diffusion DMTEF(DPMTEF)algorithm.This method reduces the single communication load per link from M to L coefficients by sharing pseudo-random coordinate selection rules and performing partial information exchange only in the combination phase.A quantitative analysis of the communication cost is also provided.Simulation results un-der typical non-Gaussian scenarios,such as mixed Gaussian noise,impulse noise,and α-stable noise,demonstrate the highest filtering accuracy of the proposed DMTEF algorithm among the compared methods.Furthermore,the DPMTEF algorithm,while significantly reducing communication overhead,still achieves a filtering performance similar to that of the DMTEF algorithm,thus realizing an effective trade-off between communication efficiency and estimation accuracy.