In consideration of the effect of ranging errors of the RSSI method on the target localization accuracy in a Wireless Sensor Networks (WSN),a mathematical model of target localization based on Least Square Support Vector Regression (LSSVR) is established according to the double mapping between the target's coordinate and the distance vector measured from the target to sensor nodes.Furthermore,the target localization method based on LSSVR in the WSN,TL-LSSVR,is proposed.According to TL-LSSVR,the training samples are formed in accordance with the virtual target coordinate and the distance vector between the virtual target and the sensor nodes,and then the training sample sets are obtained by selecting learning areas and grid sampling.Moreover,the localization model can be trained using LSSVR and the target can be located by inputting the distance vector between the target and the sensor nodes into a localization model.The experiments of target localization in the WSN under different numbers and distributions of sensor nodes are performed.Experimental results show that when sensor node distributes randomly,the target localization errors using the TL-LSSVR are reduced by 21.0%-43.1% compared with that of a least square estimation,and when sensor node distributes uniformly,the target localization errors are reduced by 26.5%-48.7%,which indicates that the target localization errors are reduced evidently,and the accuracy of target localization is improved.