{'fraction_non_alphanumeric': 0.018052869116698903, 'fraction_numerical': 0.0, 'mean_word_length': 5.959641255605381, '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}}
Efficient Direct Target Localization for Distributed MIMO Radar With Expectation Propagation and Belief Propagation

It has been shown that direct target localization in distributed multiple input multiple output (MIMO) radar can outperform indirect localization significantly, but conventional direct localization methods suffer from both high computational complexity and high communication cost. In this work, we address the issues by designing an efficient factor graph based message passing approach to direct localization, which greatly reduces the computational complexity and communication cost. First, a factor graph representation for the problem of direct localization is developed, which, however, involves difficult local functions. Inspired by expectation propagation (EP), we design an iterative method to solve the problem, where both EP and belief propagation (BP) are used to make message passing in the factor graph tractable, leading to a low complexity message passing iterative method. We show that the message passing based method are very suitable for decentralized processing and can be employed in distributed radars with different configurations. Extensive comparisons with state-of-the-art indirect and direct methods are provided, which show that the proposed method can achieve similar performance to the exhaustive search-based direct localization methods while with much lower computational complexity and communication cost, and it outperforms significantly indirect localization methods at low signal to noise ratios.