1. A method for detecting a target in a non-homogeneous environment using space-time adaptive processing of a radar signal, comprising the steps of: normalizing training data of the non-homogeneous environment to produce normalized training data, wherein the normalizing is according to x ~ k = x k x k H ⁢ x k ⁢ / ⁢ K , wherein x k is a k th training vector, K is a total number of training vectors, H is a Hermitian transpose operation, {tilde over (x)} k is a k th normalized training data vector; representing the normalized training data as a normalized sample covariance matrix, wherein the normalized sample covariance matrix {tilde over (R)} is determined based on a sub-space tracking method according to R ~ = 1 K ⁢ ∑ k = 1 K ⁢ ⁢ x ~ k ⁢ x ~ k H ; tracking a subspace represented by the normalized, sample covariance matrix to produce a clutter subspace matrix, wherein the sub-space tracking uses a clutter subspace tracking according to 
 {tilde over (R)}=Ũ MN×r λ r×r Ũ MN×r H +Iσ n 2 , wherein λ r×r is a diagonal matrix with most important r eigenvalues of the clutter subspace along the diagonal, σ n 2 is a noise variance, I is an identity matrix, Ũ MN×r is the clutter subspace matrix, M is the number of antenna elements, N is the number of pulses and r is a rank of estimated the clutter subspace covariance matrix; determining a test statistic representing a likelihood of a presence of the target in the radar signal based on the clutter subspace matrix and a steering vector; and comparing the test statistic with a threshold detect the target, wherein the steps are performed by a processor.