基于粒子群算法的3维激光雷达回波分解

为了提高3维激光雷达回波分解的精度和准确度,采用粒子群算法与最小二乘法相结合的方法,分析了激光雷达回波分解原理以及粒子群算法原理,研究了粒子群算法在激光雷达回波信号分解中的应用;进行了理论分析与实际数据验证,取得了实际激光雷达回波数据的分解结果。结果表明,采用粒子群算法与最小二乘法相结合的分解方法,激光雷达回波可以更高精度地分解为一系列单个波形的叠加,并获得了延时、强度及脉宽等参量,拟合度提高至0.989,一定程度上抑制了噪声的干扰。该算法可以有效提高激光雷达回波分解的精度。

3-D lidar echo decomposition based on particle swarm optimization

In order to improve accuracy and precision of lidar echo decomposition , the theory combining particle swarm optimization algorithm with the least squares method was used and the principles of lidar echo decomposition and particle swarm optimization algorithm were analyzed .The application of particle swarm optimization algorithm in lidar echo decomposition was studied.After theoretical analysis and experimental verification , real data of decomposition experiment was gotten .The results show that lidar echo can be decomposed into a series of single waveform by the combining method .The fitting degree was improved to 0.989 by using the parameters of time delay , intensity and pulse width .It may reduce noise interference to some extent.The result shows this algorithm is effective and feasible .