A process implemented method comprising: receiving , via one or more hardware processors, a channel impulse response H(m) captured by a Stepped Frequency Continuous Wave Multi-Input Multi-Output 'SFCW MIMO' radar; computing , via the one or more hardware processors, a covariance matrix R SF from the channel impulse response; performing , via the one or more hardware processors, eigen value decomposition of the covariance matrix to obtain a signal subspace matrix U s comprising eigen vectors corresponding to a pre-defined number of largest eigen values of the covariance matrix; constructing , via the one or more hardware processors, a pre-defined number ' N μ ' of sets of transformation matrices, wherein each set of transformation matrices comprise a left transformation matrix β left μ and a right transformation matrix β right μ computed based on a first identity matrix I N f with its first diagonal element as 0, a second identity matrix I N f with its last diagonal element as 0 and a binary matrix J μ with only the element at ( μ , μ ) as 1 and rest of the elements as 0; computing , via the one or more hardware processors, a plurality of transformed signal subspace matrices φ μ based on the sets of transformation matrices and the signal subspace matrix; calculating , via the one or more hardware processors, a sum of the plurality of signal-subspaced transformational matrices and performing eigen value decomposition on the sum to obtain a rotational signal-subspaced transformational matrix T R ; determining , via the one or more hardware processors, a parameter matrix Ω μ from the rotational signal-subspaced transformational matrix; and estimating , via the one or more hardware processors, azimuth angle, elevation angle and range of each of the plurality of targets from the parameter matrix to localize the plurality of targets. receiving , via one or more hardware processors, a channel impulse response H(m) captured by a Stepped Frequency Continuous Wave Multi-Input Multi-Output 'SFCW MIMO' radar; computing , via the one or more hardware processors, a covariance matrix R SF from the channel impulse response; performing , via the one or more hardware processors, eigen value decomposition of the covariance matrix to obtain a signal subspace matrix U s comprising eigen vectors corresponding to a pre-defined number of largest eigen values of the covariance matrix; constructing , via the one or more hardware processors, a pre-defined number ' N μ ' of sets of transformation matrices, wherein each set of transformation matrices comprise a left transformation matrix β left μ and a right transformation matrix β right μ computed based on a first identity matrix I N f with its first diagonal element as 0, a second identity matrix I N f with its last diagonal element as 0 and a binary matrix J μ with only the element at ( μ , μ ) as 1 and rest of the elements as 0; computing , via the one or more hardware processors, a plurality of transformed signal subspace matrices φ μ based on the sets of transformation matrices and the signal subspace matrix; calculating , via the one or more hardware processors, a sum of the plurality of signal-subspaced transformational matrices and performing eigen value decomposition on the sum to obtain a rotational signal-subspaced transformational matrix T R ; determining , via the one or more hardware processors, a parameter matrix Ω μ from the rotational signal-subspaced transformational matrix; and estimating , via the one or more hardware processors, azimuth angle, elevation angle and range of each of the plurality of targets from the parameter matrix to localize the plurality of targets. <EOS>