In view of the flaw that the positioning accuracy of WiFi indoor positioning technology based on position fingerprint fails to meet the practical application requirements, a WiFi indoor positioning and tracking algorithm integrating adaptive affine propagation (AAPC), compressed sensing (CS), and Kalman filtering has thus been proposed. Among them, AAPC algorithm is used to generate clustering fingerprints with the best clustering effect performance in the offline stage, followed by a position estimation during the online phase with CS and nearest neighbor algorithms adopted. Finally, localization and tracking are performed by combining Kalman filtering with physical constraints. Based on a collection of a large amount of real experimental data, it has been proven that the developed algorithm is characterized with a higher positioning accuracy and more accurate trajectory tracking effect.