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A single radio frequency (RF) channel digital beamforming (DBF) array antenna based on compressed sensing (CS) was proposed to reduce the hardware costs, power consumption, and the design complexity of large-scale DBF systems. For DBF antenna systems, the general way to obtain the signals received by each sensor is to connect each sensor to an independent RF receiver channel. When the array contains a large number of sensors, the multichannel signal sampling scheme makes the system power hungry and expensive. A time sequence phase weighting (TSPW) technology provides a solution to this problem. The TSPW antenna array can obtain the signals received by each sensor with only one RF receiver channel by sequentially sampling the specific single channel signals, which are produced by the TSPW array. However, the number of single channel samples scales linearly with the number of sensors. The sampling time will be so long as to be unacceptable when the array contains a large number of sensors. To overcome this problem, we introduced CS theory to the TSPW array. With the help of CS, the sampling frequency can be simultaneously reduced in both time and spatial domain, which correspond to the reduction of the number of both the samples and sensors. Theoretical analyses have been proposed to show the conditions that should be met for successful reconstruction. The simulation results from an X-band array with an aperture size of 80λ under the signal-to-noise ratio of 15 dB and the scenario of six targets showed that the proposed array could save above 63.1% of sensor numbers and above 84.4% in sampling time when compared with those of conventional TSPW arrays.