多角度动态光散射(MDLS)技术具有响应速度快、非接触式测量等优势，相较于单一散射角度测量技术，MDLS能获取更多的反映颗粒特征的散射光信息，可提供更准确的颗粒粒度分布(PSD)，而合适的颗粒粒度反演算法能进一步提高MDLS 方法的测量准确性。在Phillips-Twomey(PT)算法基础上，提出采用MR-L 曲线法确定正则化参数，递归算法求取权重系数并添加非负约束的递归非负Phillips-Twomey(RNNPT)算法，通过准确确定权重系数改善MDLS 颗粒粒度分布反演算法的准确性。采用RNNPT 算法，在光强相关函数测量噪声为0.01%~1%，对两种单峰模拟分布以及一种双峰模拟分布颗粒体系进行了粒度反演，可知在噪声水平低于0.1%的情况下反演结果均较为理想。将RNNPT 算法与递归Phillips-Twomey(RPT)算法和现有的递归Tikhonov(RT)算法进行比较，计算结果表明，RNNPT 算法获得的权重系数比误差最小，反演获得的粒度分布与理论值最为接近。

Besides the advantages such as the fast response speed, non-contact measurement and so on, the multiangle dynamic light scattering (MDLS) can give better particle-size distribution (PSD) because it can get more information about scattering light compared to single-angle light scattering technolgy, which can reflect the characteristic parameters of particles, and the improvement of the accuracy of MDLS depends largely on appropriate PSD inversion methods. Based on Phillips-Twomey (PT) algorithm, the Recursion Nonnegative Phillips-Twomey (RNNPT) algorithm is proposed, which uses the MR-L-curve to estimate the regularization parameter, recursion algorithm to obtain the weighting coefficients and the addition of the nonnegative constraint. Through the exact determination of the weighting coefficients, RNNPT algorithm can improve the accuracy of MDLS PSD inversion algorithm. When the light intensity autocorrelation function noise range is from 0.01% to 1%, the results show that the inverted PSDs of the two unimodal distribution and the bimodal distribution are comparatively desired with the noise level under 0.1%. In contrast to recursion Phillips-Twomey (RPT) and the existing recursion Tikhonov (RT) algorithm, the calculation results show that the weighting coefficient ratio obtained from RNNPT algorithm is minimum and the inverted PSD is closest to the theoretical one.