Abstract: In order to autonomously reckon location and azimuth for unmanned combat aerial vehicle (UCAV)’s cognitive navigation, a kind of path integration based on multi-scale grid cells was proposed. According to the characteristics of neighboring cells in dorsal medial entorhinal cortex (dMEC) sharing common firing traits, while the firing traits of the grid increases isometrically along the dorsoventral axis, a bionic grid groups with incremental scales was constructed firstly, and then synaptic pattern was introduced in each grid layer to evaluate cell weights, therefore, the location and azimuth in each grid layer were calculated by changeable cell activities, and path integration was ultimately achieved in each grid layer. Space location precision was further improved by using integration results of small scales to adjust the grid layer of bigger scale. Simulation results prove that the method can exactly reckon location and azimuth within certain velocity error and azimuth error. It has a higher space location reckoning by adjusting bigger-scale grid layer. And the azimuth error has an indentation distribution accompanying the moving direction changing.