Since the 1950s, specular meteor radars (SMRs) have been used to study the mesosphere and lower thermosphere (MLT) dynamics. Atmospheric parameters derived from SMRs are highly dependent on the number of detected meteors and the accuracy of the meteors’ locations. Recently, incoherent and coherent multiple-input-multiple-output (MIMO) radar approaches combined with waveform diversity have been proposed to increase the number of detected meteors and to improve time, altitude, and horizontal resolution of the estimated wind fields. The incoherent MIMO approach refers to the addition of new transmit sites (widely separated), whereas the coherent MIMO refers to the addition of new transmit antennas in the same site (closely separated). In both the cases, a different pseudorandom sequence is transmitted from each antenna element. Unfortunately, the addition of new transmit antennas with different code sequences degrades the performance of conventional signal recovery algorithms. This is a consequence of the cross-interference between the transmitted waveforms, making it worse as the number of transmitters increases. In this article, we propose a signal recovery approach based on compressed sensing, taking advantage of the sparse nature of specular meteor echoes. The approach allows the exact recovery of weak echoes even in interference environments. Besides the advantage of the proposed approach to recover the meteor signal, we discuss the optimal selection of the transmitted waveforms and the minimum code length required for exact recovery. Additionally, we propose a modification of the orthogonal matching pursuit algorithm used in sparse problems to make it applicable in real-time analysis of large data. The success of the proposed approach is corroborated using Monte Carlo simulations and real data from a multi-static spread spectrum meteor radar network installed in northern Germany.