基于改进非负矩阵分解的多组分气体光谱解混算法

Multi-component gas spectral demixing algorithm based on improved non-negative matrix factorization

从重叠情况严重的混合气体光谱中解析出单一纯光谱数据,一直是光谱解析的难点。为了得到理想的解混精度,采用改进的非负矩阵分解算法,引入光谱的相关性约束与平滑性约束,并给出优化的梯度下降法的迭代步长,以避免算法收敛到局部不稳定点带来的影响.改进的算法既综合了矩阵的分解误差,又考虑了混合光谱特性的影响。实验数据表明,改进的非负矩阵分解得到的解混结果能够准确解析出各源光谱的特征峰形状,并且各解混结果之间几乎没有混合叠加影响部分,可以满足后续的光谱识别工作。

It is always difficult to extract the single pure spectral data from the mixed gas spectrum with severe overlap.In order to obtain the ideal unmixing precision,an improved non-negative matrix factorization algorithm is proposed,in which the correlation constraint and smoothness constraint of the spectrum is introduced,and the iterative step size of the optimized gradient descent method is given to avoid the effects of algorithm convergence to local instability.The improved algorithm combines the decomposition error of the matrix and influence of the mixed spectral characteristics.The experimental data show that the demixing results obtained by the improved non-negative matrix factor can accurately resolve the characteristic peak shape of each source spectrum,and there is almost no mixed superimposed influence between the demixing results,which can satisfy the subsequent spectral recognition work.