<p>Space-time adaptive processing (STAP) serves as a potent tool for clutter suppression and moving target detection within airborne radar systems. Estimation of the clutter covariance matrix (CCM) stands as a pivotal challenge in STAP filter design and achieving sparse reconstruction of the clutter covariance matrix with a limited number of samples is of paramount importance. This paper presents a clutter suppression method tailored for airborne forward-looking array radar systems, grounded in principles of joint statistical analysis and structural prioritization. This approach facilitates the estimation of the clutter covariance matrix even in scenarios characterized by sample scarcity. Leveraging the assumption of adherence to an inverse Wishart prior distribution, the methodology derives a maximum posterior estimate by exploiting the intrinsic low-rank symmetry of the matrix. Simulation results, conducted using a radar forward-looking array model, demonstrate the efficacy of the proposed method in enhancing clutter suppression performance compared to traditional covariance matrix estimation techniques, all while maintaining computational efficiency.</p>