{'fraction_non_alphanumeric': 0.022388059701492536, 'fraction_numerical': 0.0, 'mean_word_length': 5.631868131868132, '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}}
Localization in wireless imaging sensor networks using SAR techniques

In wireless sensor networks, localization and routing are challenging problems with many research attentions. In dense sensor networks, overhead and delay for routing significantly decrease throughput of the system. Also when sensors gather massive information about environment such as image, the throughput of the system dramatically reduces. In this paper, we introduce a new method which is combination of radar localization techniques and clustering approach that can increase the throughput and total capacity in dense sensor networks. We adopt non-coherent method, like Matched-Filter (MF) and ML-estimation, to analyze the Synthetic-Aperture-Radar (SAR)-like topology. In localization method, one important advantage is transferring complexity from sensor nodes to Header and then to Base Station (BS) node which has sufficient energy and calculation power to analyze received information. Moving BS in two directions instead of one achieves less calculation complexity in comparison with other methods for the same resolution. Accordingly we can improve the performance of network using cooperation and aggregation techniques.