{'fraction_non_alphanumeric': 0.012396694214876033, 'fraction_numerical': 0.0, 'mean_word_length': 5.05625, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Performance analysis of the MUSIC algorithm

The MUSIC algorithm is one of the more important high resolution approaches for direction finding and spectral estimation that have been developed in recent years. Asymptotically (i.e. infinite data or SNR) the MUSIC algorithm has been shown to yield efficient unbiased estimates. However the performance of the algorithm for the non-asymptotic situation of high noise and limited data has not been fully addressed. In this paper we study the performance of the MUSIC algorithm when only finite noise corrupted data is available. We focus on the role of array design in the performance of MUSIC algorithm for direction finding and introduce certain measures to characterize its performance. We show that in the single target situation these measures can be described in terms of the familiar conventional beampatterns. Results of computer simulations carried out to check the usefulness of such measures are also presented.