In this paper, we examine a high resolution direction of arrival (DOA) estimation techniques. Among of these algorithms which can yield accurate DOA estimates for multiple narrow band sources are Gabierl's thermal noise, aduptive angular response, and maximum-likelihood method. These algorithms are capable of detecting pulse that exists for short periods of time. Under this environment, the performance of different adaptive superresolution array algorithms that utilize the Howell-Applebaum technique is compared. Two different cases are discussed. Firstly , the adaptive array is subjected to only one signal plus thermal noise. Secondly, the case of the presence of two incident signals plus thermal noise is investigated. It is shown that every algorithm offers its best performance depending upon the incident signal parmeters, number of antennas, the range of input signal-to-noise ratio, and the angle of separation between the incoming signals.
In recent years, considerable effort has been spent in developing high adaptive array superresolution techniques. This paper presents a perturbation expansion method named "two-variable technique" to derive an expression for the output power of the adaptive null-steering antenna array considering the statistical weight fluctuations. This array utilizes Howells-Applebaum type adaptive weight. Thus, in turn, the paper presents the difference between the assumed and actual adaptive array signal processing response of different high resolution techniques. These techniques are based on maximum-likelihood method, adaptive-angular response, and thermal noise algorithm. By evaluating the model performance with different signal-to-noise ratios, number of antennas, and different signal arrival angles for selected test cases, we gain insights into the sensitivity of the signal estimation problem to model uncertainty.
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