استفاده از الگوریتم مصنوعی کلونی زنبور عسل برای برآورد حداکثر احتمال جهت رسیدن به مقصد

Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation, but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use. To reduce the high computational burden of ML method and make it more suitable to engineering applications, we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation. As a recently proposed bio-inspired computing algorithm, ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment. It offers an excellent alternative to the conventional methods in ML-DOA estimation. The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence, Signal-to-Noise Ratio (SNR), and number of iterations. The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated. Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.

The estimation of Direction-Of-Arrival (DOA) is an important problem in array signal processing, which can be widely used in the areas of radar, sonar, seismology, wireless communication, etc.. It has attracted great amount of interest for decades, and many useful estimation methods have been proposed and analyzed, including the Maximum Likelihood (ML) methods[1], the Multiple Signal Classification (MUSIC) methods[2], the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT)[3], etc.. The ML method is an excellent statistically effective and robust estimation technique. Its performance is better than the subspace decomposition class methods such as MUSIC and ESPRIT, especially under the conditions of lower Signal- to-Noise Ratio (SNR) or smaller snapshot number. Furthermore, ML method can estimate the parameters effectively when the sources are coherent signals, in which condition the subspace decomposition class methods will lose efficiency. While we can get the optimal DOA angles through the ML method theoretically, but the ML estimator requires the maximization of a nonlinear multimodal likelihood function. Since it requires multidimensional solution search which makes the operation much more complicated, the application of the ML method is restricted. Due to the characteristics of the ML method, many alternative multidimensional searching methods have been proposed in the past decades to reduce the the computational complexity, such as the Alternating Projection (AP) method[4], Space-Alternating Generalized Expectation-maximization (SAGE) method[5], Method Of Direction Estimation (MODE)[6], MODEX[7], and modified MODEX[8], etc.. Unfortunately, these methods still have some drawbacks that restrict their applications. The AP method converts the multidimensional searching to unidimensional searching, but its convegence becomes rather slow when the source number increases.The SAGE method requires detailed knowledge of the

In this section, we demonstrate the simulation results of the convergence properties of the proposed method. Then, we compare the proposed method withgrid search method and other popular bio-inspiredcomputing algorithms in statistical performance andcomputational load. We choose three meta-heuristicmethods including DE, PSO and CLONALG representatively to compare with the proposed method forML-DOA estimation. For the sample data, the receiver array is supposed to be a 10 sensor uniform linear array,and 100 data snapshots are used