Space-time adaptive processing (STAP) is a signalprocessing technique most commonly used in radar systems where interference is a problem. The radar signal processor is used to remove the unintentional cluttering effects caused by ground reflections and echoes due to sea, desert, forest, etc. and intentional jamming and make the received signal useful. In this paper a new approach to STAP based on subspace projection has been described in detail. According to linear algebra and three dimensional geometry, if we project a range space on to a subspace spanned by linearly independent vectors, we can suppress data which is perpendicular to that subspace. In subspace based technique, the received data is projected on to a subspace which is orthogonal to clutter subspace to remove the clutter. The probability of target detection can be find out in order to analyse the performance of the proposed algorithm. Two existing algorithms, SMI and DPCA are chosen to do the comparison. while plotting the detection Probability against SINR , the results obtained are better for subspace technique than DPCA and SMI. We got the SINR improved for subspace based technique for same detection probability. The effect of subspace rank on SINR was also analysed for understanding the computational load caused by the technique. We also analysed the convergence of the algorithm by taking plots of SINR against range snapshots.