FPSO algorithm for the ML estimation of 2-DOA

the Maximum Likelihood 2-DOA estimation is a kind of method which is high accurate and applicable in situation with coherent signals and low SNR. The root of its ML function is difficult to get, which is likely to converge to local optimized root. Allowing for that PSO method has characteristics of converging to global optimized root and enormous computational complexity, a new method named FPSO is proposed in this paper, which greatly lower the computational complexity and keep the property of global convergence. However, if FPSO algorithm wants to arrive at highly 2-DOA estimation accurate when signals suffer form multi-path effect, it needs to larger its iteration number. In order to get much lower computation, a modified FPSO method is put forward. It costs lower computation in above bad condition because of the more accurate initialization of global optimum particle get from AP algorithm. Simulations prove that FPSO and modified FPSO algorithm could accomplish accurate 2-DOA estimation at a cost of low computational complexity respectively in AWGN and HF channel. Meanwhile, their RMSE approximate each CRB with increase of SNR and the number of iteration.