In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according t...In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach.展开更多
The localization performance of matched-field processing decreases substantially for high-mismatch and high-frequency localization scenarios.Based on incoherent frequency-difference source localization technique and d...The localization performance of matched-field processing decreases substantially for high-mismatch and high-frequency localization scenarios.Based on incoherent frequency-difference source localization technique and data-driven machine learning localization method,this paper proposes a convolutional neural network localization method based on the cross-spectral density matrix of coherent frequency-difference signal.This method utilizes dual-driven modeling and data to localize mid-to high-frequency underwater sources in mismatched and noisy scenarios.For frequency-difference signals,this method performs incoherent averaging along the central frequency dimension to reduce the influence of cross-term,and stacks along the frequency-difference dimension to preserve the correlation information between different frequencies.The resulting tensor data is then used as input to a convolutional neural network for underwater source localization.This paper conducts experiments using the SwellEx-96 simulation environment and sea trial data from the 2021 South China Sea Acoustic Tomography Long-Range Experiment.The experiments show that the proposed localization algorithm is suitable for localizing medium and high frequency sources in the noise-containing and mismatch-existing marine environment,showing favorable tolerance for underwater source localization.展开更多
Many applications of wireless sensor networks can benefit from fine-grained localization. In this paper, we proposed an accurate, distributed localization method based on the time difference between radio signal and s...Many applications of wireless sensor networks can benefit from fine-grained localization. In this paper, we proposed an accurate, distributed localization method based on the time difference between radio signal and sound wave. In a trilateration, each node adaptively chooses a neighborhood of sensors and updates its position estimate with trilateration, and then passes this update to neighboring sensors. Application examples demonstrate that the proposed method is more robust and accurate in localizing node than the previous proposals and it can achieve comparable results using much fewer anchor nodes than the previous methods.展开更多
基金partially supported by the National Natural Science Foun-dation of China(No.62071389).
文摘In this paper,an effective target locating approach based on the fingerprint fusion posi-tioning(FFP)method is proposed which integrates the time-difference of arrival(TDOA)and the received signal strength according to the statistical variance of target position in the stationary 3D scenarios.The FFP method fuses the pedestrian dead reckoning(PDR)estimation to solve the moving target localization problem.We also introduce auxiliary parameters to estimate the target motion state.Subsequently,we can locate the static pedestrians and track the the moving target.For the case study,eight access stationary points are placed on a bookshelf and hypermarket;one target node is moving inside hypermarkets in 2D and 3D scenarios or stationary on the bookshelf.We compare the performance of our proposed method with existing localization algorithms such as k-nearest neighbor,weighted k-nearest neighbor,pure TDOA and fingerprinting combining Bayesian frameworks including the extended Kalman filter,unscented Kalman filter and particle fil-ter(PF).The proposed approach outperforms obviously the counterpart methodologies in terms of the root mean square error and the cumulative distribution function of localization errors,espe-cially in the 3D scenarios.Simulation results corroborate the effectiveness of our proposed approach.
基金supported by the National Natural Science Foundation of China(62071429).
文摘The localization performance of matched-field processing decreases substantially for high-mismatch and high-frequency localization scenarios.Based on incoherent frequency-difference source localization technique and data-driven machine learning localization method,this paper proposes a convolutional neural network localization method based on the cross-spectral density matrix of coherent frequency-difference signal.This method utilizes dual-driven modeling and data to localize mid-to high-frequency underwater sources in mismatched and noisy scenarios.For frequency-difference signals,this method performs incoherent averaging along the central frequency dimension to reduce the influence of cross-term,and stacks along the frequency-difference dimension to preserve the correlation information between different frequencies.The resulting tensor data is then used as input to a convolutional neural network for underwater source localization.This paper conducts experiments using the SwellEx-96 simulation environment and sea trial data from the 2021 South China Sea Acoustic Tomography Long-Range Experiment.The experiments show that the proposed localization algorithm is suitable for localizing medium and high frequency sources in the noise-containing and mismatch-existing marine environment,showing favorable tolerance for underwater source localization.
文摘Many applications of wireless sensor networks can benefit from fine-grained localization. In this paper, we proposed an accurate, distributed localization method based on the time difference between radio signal and sound wave. In a trilateration, each node adaptively chooses a neighborhood of sensors and updates its position estimate with trilateration, and then passes this update to neighboring sensors. Application examples demonstrate that the proposed method is more robust and accurate in localizing node than the previous proposals and it can achieve comparable results using much fewer anchor nodes than the previous methods.
