On the Performance of the Constant Modulus Array Restricted to the Signal Subspace

The Constant Modulus Array has a slow rate of convergence mainly due to both the nonconvex nature of its cost function and the well known behavior of stochastic steepest descent algorithms for environments with a large eigenvalue spread. In this paper we analyze the solutions of the Constant Modulus Cost Functions, showing that the weight vectors associated to its minima lie on the signal subspace. From that information we develop a modified version of the Constant Modulus Array, which speeds up the convergence and reduces the final misadjustment error. The proposed method is specially useful for arrays having a large number of sensors and low Signal to Noise Ratio for the Source of Interest.