为了提高传感器测量土壤水分的精确度，提出改进粒子群算法 (IPSO)。首先粒子个体最优值利用高斯变换，提高了算法的局部搜索能力，粒子全局最优值采用柯西变换，吸引其他粒子到更好的搜索空间区域，提高 了算法的全局搜索能力；接着混沌函数动态调节惯性权重，在迭代初期具有较大的值便于快速寻优，而在算法后期有较小的值，放慢搜索速度，以便进行精确寻优。实验仿真显示本文算法对测量砾石脱湿、吸湿数据的均方 误差 (MSE)以及 Pearson相关系数相比其他算法都较好，其中脱湿实测数据在基质势为 1 000 cm时，IPSO算法 MSE均值为 16.62×10-6，相比 LSM,FOA,HSA,PSO,SAA分别减少 75.59%,66.67%,63.53%,53.73%,57.53%；吸湿实测数据在基质势为 1 000 cm时，IPSO算法 MSE均值为 10.21×10-6，相比 LSM,FOA,HSA,PSO, SAA分别减少 81.42%,75.29%,72.00%,65.57%,67.69%。

In order to improve the accuracy of sensor measuring soil moisture, Improved Particle Swarm Optimization(IPSO) algorithm is proposed. Firstly, Gauss transform is utilized to improve the local search ability, and Cauchy transform is adopted to attract other particles to better search space area, which improves the global search ability. Secondly, Chaotic function is adjusted the inertia weight dynamically, it has larger value in the initial iteration stage and smaller value in the later iteration stage, and the searching speed is slowed down in the later iteration. The simulation results show that the Mean Square Error(MSE) and Pearson correlation coefficients of IPSO algorithm are better than that of other algorithms for measuring gravel dehumidification and moisture absorption data, the MSE of the measured data of dehumidification for IPSO at the substrate potential of 1 000 cm is 16.62×10-6, which is 75.59%, 66.67%, 63.53%, 53.73% and 57.53% lower than that for LSM, FOA, HSA, PSO and SAA respectively. For the measured data of moisture absorption at the substrate potential of 1 000 cm, MSE of IPSO is 10.21×10-6, which is 81.42%, 75.29%, 72.00%, 65.57% and 67.69% lower than that of LSM, FOA, HSA, PSO and SAA respectively.