ed at the disadvantages of the lower azimuth resolution of the sky-wave radar and larger position error of traditional analytic algorithm, a new locating model using chaotic mutation grey wolf optimization algorithm to optimize the kernel extreme learning machine (KELM) is put forward. First, the piecewise linear chaotic map, adaptive Cauchy mutation strategy and non-linearity of the convergence factor are introduced into the grey wolf optimization algorithm to form an improved grey wolf algorithm. Then, the improved grey wolf optimization algorithm is used to optimize penalty coefficient and kernel parameter of the KELM. Finally, the optimized the KELM is applied to sky-wave radar location, making the established KELM model have the high steady-state prediction accuracy and generalization performance. The experimental results show that the predicted results of the proposed model are basically consistent with the measured values, and the prediction accuracy is higher than that of the KELM location model, which is optimized by the standard grey wolf algorithm. A new target location method is provided for sky-wave radar.