目标表面双向反射分布函数(BRDF)可用于描述目标的散射特性,被广泛应用于目标探测识别、特征分析提取等领域。基于五参量BRDF模型,针对目标表面材质特点建立了两类材料的简化五参量模型(半光面材料BRDF模型和毛面材料BRDF模型)。在此基础上,优化了模型参量拟合算法,在粒子群优化算法(PSO)中引入模拟退火算法(SA),提高了算法的局部搜索能力和效率。实验测量了不同粗糙度的2024铝合金样片和三种大粗糙度样片的BRDF数据,分析了目标样片BRDF随散射角的变化趋势以及其与表面粗糙度的关系。利用模拟退火粒子群优化算法(SAPSO)、遗传算法(GA)、粒子群算法对实验数据进行拟合,模型计算结果与实验结果的对比显示,模拟退火粒子群算法拟合误差更小,更适用于BRDF优化建模,证明了该算法的有效性。

The bidirectional reflectance distribution function (BRDF) of target surface can be used to describe the scattering characteristics of targets, which has been widely used in the applications such as target detection and identification, feature analysis and extraction. In this paper, based on five parameter BRDF model, two simplified five parameter models (BRDF model of semi glossy material and BRDF model of rough surface material) are presented according to the characteristics of different target surface material attributes. Furthermore, the model parameter fitting algorithm is optimized. The simulated annealing algorithm (SA) is adopted in the particle swarm optimization algorithm (PSO) to enhance the local search ability of the algorithm and improve the efficiency of the algorithm. The BRDF data of 2024 aluminum alloy samples with different roughnesses and three kinds of large roughness samples are experimentally measured. The trend of the BRDF variation of target sample with scattering angle, the relationship between the BRDF of target sample and surface roughness, are analyzed. Simulated annealing particle swarm optimization algorithm (SAPSO), genetic algorithm (GA) and particle swarm algorithm are used to fit the experimental data. The comparison between the model calculation results and the experimental results shows that the simulated annealing particle swarm algorithm has a smaller fitting error and is more suitable for BRDF optimization modeling, which proves the effectiveness of the algorithm.