A kernel ridge regression (KRR) based localization algorithm is proposed for localization in wireless sensor networks. KRR algorithm adds kernel function on the basis of ridge regression. In the off-line phase of the algorithm, the KRR method is used to extract the nonlinear relationship between fingerprint data of all positions, and the nonlinear regression positioning model is trained. The received signal strength indicator(RSSI) value of the target is collected in the online stage, and the target position is estimated using the positioning model. Simulation analyzes various factors which of impact the algorithm performance. The location experiment is achieved in indoor typical office environment. Experimental results show that under the influence of different factors, this algorithm can achieve better positioning accuracy than the traditional WKNN algorithm. When the position grid spacing is 1.8 m, the average positioning error of WKNN algorithm is 2.53 m, while the error of the proposed algorithm is 1.58 m.