A new reduced dimension space-time adaptive processing algorithm that exploits low rank clutter(LRC-RD)is proposed to suppress clutter and jamming for airborne MIMO radar. Firstly

a clutter subspace matrix is constructed off-line with known radar system parameters

and then the transform matrix with reduced dimension is generated by combining the jamming plus noise covariance matrix with the target space-time steering vector. Finally

adaptive weights are calculated from the data with reduced dimension. The data dimension after dimension reducing equals clutter rank plus one. Thus the computational cost and the number of samples for computing adaptive weights are reduced apparently which make LRC-RD converge fast

and the theoretical performance of LRC-RD can reach that of full dimensional adaptive processing. Numerical results show that when the number of training samples is double of the dimension reduced by LRC-RD and there is no array errors

and the signal to interference plus noise ratio loss of the proposed algorithm is about 5 dB and 17 dB higher than those of the bi-iteration based algorithm and the sub-array based algorithm

respectively.