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Real world signals are non-stationary but can be modeled as stationary within the local time frames. These types of signals are called quasi stationary signals (QSS). In this paper a Khatri-Rao (KR) subspace based direction of arrival (DOA) estimation of QSS is considered by designing a coprime array structure. This structure provides an alternative way to enhance the degrees of freedom (DOF) and it can also eliminate mutual coupling effects. One of the most important observations is that the covariance matrix can be extended based on non-circularity of QSS. The covariance matrix exhibits non-circularity due to the non-circular behavior of QSS. Exploiting the non-circularity an extended covariance matrix (ECM) is designed to achieve higher DOF. Hence, the proposed algorithm has the capability to uniquely estimate DOA's more than twice the number of sensors. Simulation results show that the proposed algorithm can achieve better performance as compared to Khatri-Rao (KR) subspace, coprime array with displaced arrays (CADiS) and nested array based techniques under various situations. KeywordsQuasi stationary signals, Khatri-Rao, extended covariance matrix, degrees of freedom, mutual coupling, and direction of arrival.