问题

Please output this exact text, with no changes at all except for fixing the misspellings. Please leave all other stylistic decisions like commas and US vs British spellings as in the original text.

We inctroduce a Bayesian estimation approach forther passive localization of an accoustic source in shallow water using a single mobile receiver. The proposed probablistic focalization method estimates the timne-varying source location inther presense of measurement-origin uncertainty. In particular, probabilistic data assocation is performed to match tiome-differences-of-arival (TDOA) observations extracted from the acoustic signal to TDOA predicitons provded by the statistical modle. The performence of our approach is evaluated useing rela acoustic data recorded by a single mobile reciever.

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金声玉亮2.0 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
deepseek-r1

We introduce a Bayesian estimation approach for further passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.		 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
gpto1 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
gpt-4o-2024-08-06 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
DeepSeek-Coder-V2-Lite-Instruct We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
Meta-Llama-3.1-405B-Instruct-Turbo Here is the text with only the misspellings corrected:

We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.		 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
Meta-Llama-3.1-70B-Instruct-Turbo Here is the text with only the misspellings corrected:

We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.		 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
Qwen2-72B-Instruct We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
baichuan4 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic localization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
doubao We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
gpto1 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
kimi We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
minimax We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
wenxinyiyan We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
zhipu We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.
deepseek-v3 We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver. We introduce a Bayesian estimation approach for the passive localization of an acoustic source in shallow water using a single mobile receiver. The proposed probabilistic focalization method estimates the time-varying source location in the presence of measurement-origin uncertainty. In particular, probabilistic data association is performed to match time-differences-of-arrival (TDOA) observations extracted from the acoustic signal to TDOA predictions provided by the statistical model. The performance of our approach is evaluated using real acoustic data recorded by a single mobile receiver.