Since the anomalies are unknown targets with low probabilities of occurrence which are significantly different from their neighbors, anomaly detection could be considered as one of the most important information extraction approaches from hyperspectral data. Various types of parametric and non-parametric algorithms have been developed in this area from the 1990's decade. Recently, sparse representation methods have been introduced and successfully accepted as a useful tool for anomaly detection based on the recovery of the majority of high-dimensional signals via a low-dimensional subspace through a dictionary of normalized signals called atoms. In other words, having a dictionary composed of bases denoting the background subspace enables the accurate recovery of background signals. Moreover, the presence of anomaly signals, assuming their deviation from the background subspace, will not have a precise estimation by the background dictionary. Hence the main idea of these anomaly detection methods is focused on evaluating recovery errors of signals by a dictionary that describes the background subspace. In such procedure, removing the atoms that describe the anomaly in the background dictionary can be considered as one of the essential actions. To this aim making diversity in the definition of spatial neighborhoods of spectral signals, as well as voting-based judgment in different situations of the spatial distribution could be proposed. In other words, by designing an optimized local dictionary, based on a local sliding window, the votes of each signal in terms of anomaly presence in each spatial neighborhood could be calculated with the aim of achieving better judgment. In this paper, a new anomaly detector for hyperspectral images is proposed based on simultaneous sparse representation using a new structured sliding window. The main contribution of this research is to improve the judgments about the anomaly presence probability using information collected during transition of the mentioned sliding window for each pixel under test. In this algorithm, each pixel experiences various spatial positions with respect to the neighbors through the transition of the sliding window. In each position, an optimized local background dictionary is molded using a well-known K-SVD method as an iterative process and the recovery error of sparse coding for each pixel under test is calculated using a simultaneous orthogonal matching pursuit algorithm (SOMP). So, the votes of each pixel in terms of the anomaly presence in each neighborhood are calculated and finally the variance of these estimated errors is considered as the anomaly detection criterion. The experimental results of the proposed method using four datasets (synthetic and real datasets) proved its higher performance compared to the GRX, LRX, CRD and BJSR detectors with an average efficiency improvement of about 9%. In addition automatic tuning of the proposed algorithm parameters (level of sparsity and the size of sliding window) and developing parallel processing techniques to improve the running time of this algorithm are the focus of our future research. It is notable that the implementation of this idea and its success showed that development of voting algorithms and the combination of the results could be considered as an efficient approach could also be utilized in other hyperspectral image processing algorithms.