The non-negative least squares algorithm is widely used in dynamic light scattering, however, it suffers the shortcomings of sparse solutions and poor anti-noise performance. It is found that the construction of the kernel matrix of the algorithm is of great significance to the accuracy of the final results. In this paper, we propose an adaptive strategy to find the optimal step-size to construct the kernel matrix. By calculating the L2-norm between the measured and reconstructed light intensity autocorrelation function, the optimal step-size is determined corresponding to the minimum difference error. Then the kernel matrix constructed by this optimal step-size is used in the inversion. The final result is obtained by fitting the solution to a predefined model, such as a Gaussian model. This strategy improves the sparsity of NNLS solution, and thus the accuracy and stability of NNLS inversion results. The simulation and experiments demonstrate that the adaptive step-size strategy improves the stability and accuracy of the non-negative least squares inversion algorithm for dynamic light scattering.

非负最小二乘算法广泛应用于动态光散射，但存在解稀疏、抗噪性能差的缺点。发现算法核矩阵的构建对最终结果的准确性具有重要意义。在本文中，我们提出了一种自适应策略来寻找构建核矩阵的最佳步长。通过计算测量的和重构的光强自相关函数之间的 L2 范数，确定对应于最小差异误差的最佳步长。然后由这个最优步长构造的核矩阵用于反演。最终结果是通过将解决方案拟合到预定义模型（例如高斯模型）来获得的。该策略提高了 NNLS 解决方案的稀疏性，NNLS 反演结果的准确性和稳定性。仿真和实验表明，自适应步长策略提高了动态光散射非负最小二乘反演算法的稳定性和准确性。