As a simplified version of Spatial Modulation （SM）， Generalized Space Shift Keying （GSSK） has been widely used in massive Multiple-Input Multiple-Output （MIMO） systems. It can better solve the problems such as Inter-Channel Interference （ICI）， Inter-Antenna Synchronization （IAS）， and multiple Radio Frequency （RF） links in traditional MIMO technology. To solve the problem of high computational complexity of the Maximum Likelihood （ML） detection algorithm for GSSK systems， a low-complexity GSSK signal detection algorithm based on Compressed Sensing （CS） theory was proposed by combining Subspace Tracking （SP） and ML detection algorithms in CS， and presetting the threshold. First， the improved SP algorithm was used to obtain partial Transmit Antenna Combinations （TACs）. Secondly， the set of search antennas was shrunk by deleting partial antenna combinations. Finally， the ML algorithm and the preset threshold were used to estimate the TACs. The results of simulation experiments show that the computational complexity of the proposed algorithm is significantly lower than that of ML detection algorithm， and the Bit Error Rate （BER） performance is almost the same as that of ML detection algorithm， which verify the effectiveness of the proposed algorithm.