IEEE 802. 11-2016 defines the fine time measurement protocol, which uses signal round trip time (RTT) to achieve indoor WiFi positioning accuracy at the meter level. However, in non line of sight or multipath environments, the accuracy of RTT ranging decreases, which seriously affects the positioning performance. To improve the accuracy of RTT positioning, this article proposes a method to convert the WiFi RTT ranging sequences measured by multiple access points into the multi-channel image, and uses an efficient channel attention-convolutional neural network to learn the relationship between the ranging data and the target position based on the multi-channel image. The experiments show that the positioning error of the proposed model is about 1 m, and 31. 03% , 16. 78% , and 10. 68% less than the conventional deep neural networks positioning, the single-channel-image-based CNN positioning, and the single-channel-image-based ECA-CNN positioning, respectively. Keywords:indoor positioning; attention mechanism; convolutional