Measurements acquired by low cost, low weighted, portable ultra-wide-band (UWB) radar systems are highly affected by measurement noise and clutter compared to the anechoic chamber vector network analyzer (VNA) measurements. Efficient background subtraction is vital prior to any target detection and recognition application. Low rank and sparse decomposition (LRSD) methods can be used to decompose radar data into its low rank and sparse components corresponding to target and background. In this study, instead of using robust principal component analysis (RPCA) which has high complexity due to successive singular value decomposition (SVD) operations in its iterations, we propose a tensor based method for background subtraction. Target response corresponding to an antenna location is recast into a matrix form with a size much smaller than the original data matrix. The concatenation of the responses for all antenna locations through the synthetic aperture form the data tensor which is divided using tensor RPCA (TRPCA). Experimental results show that the proposed method outperforms RPCA visually for appropriate choices of the regularization parameter with a decrease of 4-16 times in computation time.