The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time perfo...The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time performance.In this paper,we propose a fingerprint positioning method based on a deep convolutional neural network(DCNN)using a classification approach in a single-base station scenario for massive multiple input multiple outputorthogonal frequency division multiplexing(MIMO-OFDM)systems.We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation.The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences.First,the DCNN model is used to determine the sub-area to which the mobile terminal belongs,and then the weighted K-nearest neighbor(WKNN)matching algorithm is used to estimate the position within the sub-area.The positioning performance is simulated in a DeepMIMO indoor environment,showing that the classification DCNN method reduces the positioning time by 77.05%compared to the non-classification method,with only a 1.08%increase in average positioning error.展开更多
High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based...High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.展开更多
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
With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors....With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors.By means of NB-IoT,the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks,thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications.Among the functions of interest for sensor networks,the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest.The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals,which therefore does not require further hardware implementations on board the nodes.This procedure,based on the RF fingerprinting technique and on machine learning processing,has been tested experimentally and has achieved interesting performances.展开更多
High-precision indoor positioning offers valuable information support for various services such as patient monitoring,equipment scheduling management,and laboratory safety.A traditional indoor positioning technology,f...High-precision indoor positioning offers valuable information support for various services such as patient monitoring,equipment scheduling management,and laboratory safety.A traditional indoor positioning technology,fingerprint indoor positioning,often employs the K-nearest neighbor(KNN)algorithm to identify the closest K reference points(RPs)via the received signal strength(RSS)for location prediction.However,RSS is susceptible to environmental interference,leading to the selection of RPs that are not physically the closest to the user.Moreover,using a fixed K value is not the optimal strategy.In this work,we propose a novel approach,the dynamic Knearest neighbor method based on strong access point(AP)credibility(SAPC-DKNN),for indoor positioning.In SAPC-DKNN,we leverage prior knowledge of RSS path loss and employ the RSS fluctuation area to quantify the significance of different APs.We integrate the similarity of AP sets within the range of strong APs and formulate a weighted distance metric for RSS based on the credibility of strong APs.Additionally,we introduce a dynamic K-value algorithm based on neighbor density(ND-DKA)for the automatic optimization of the K value for each test point.Experimental evaluations conducted on three datasets demonstrate that our method significantly reduces the average positioning error by 15.41%–64.74%compared to the state-of-the-art KNN methods.展开更多
基金supported by the National Key Research and Development Program of China(No.2022YFC3801000)the Fundamental Research Funds for the Central Universities(No.2242022k60001,2242023K40015).
文摘The combination of fingerprint positioning and 5G(the 5th Generation Mobile Communication Technology)offers broader application prospects for indoor positioning technology,but also brings challenges in real-time performance.In this paper,we propose a fingerprint positioning method based on a deep convolutional neural network(DCNN)using a classification approach in a single-base station scenario for massive multiple input multiple outputorthogonal frequency division multiplexing(MIMO-OFDM)systems.We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation.The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences.First,the DCNN model is used to determine the sub-area to which the mobile terminal belongs,and then the weighted K-nearest neighbor(WKNN)matching algorithm is used to estimate the position within the sub-area.The positioning performance is simulated in a DeepMIMO indoor environment,showing that the classification DCNN method reduces the positioning time by 77.05%compared to the non-classification method,with only a 1.08%increase in average positioning error.
基金supported in part by the National Natural Science Foundation of China under Grant No.61771474in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX212243+2 种基金in part by the Young Talents of Xuzhou Science and Technology Plan Project under Grant No.KC19051in part by the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University under Grant No.2021D02in part by the Open Fund of Information Photonics and Optical Communications (IPOC) (BUPT)。
文摘High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.
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.
文摘With the recent introduction of NarrowBand Internet of Things(NB-IoT)technology in the 4th and 5th generations of mobile radio networks,the mobile communications context opens up significantly to the world of sensors.By means of NB-IoT,the mobile systems within 3GPP standardization introduce the peculiar functions of sensor networks,thus making it possible to satisfy very specific requirements with respect to those which characterize traditional mobile telecommunications.Among the functions of interest for sensor networks,the possibility of locating the positions of the sensors without an increase in costs and energy consumption of the sensor nodes is of utmost interest.The present work describes a procedure for locating the NB-IoT nodes based on the quality of radio signals received by the mobile terminals,which therefore does not require further hardware implementations on board the nodes.This procedure,based on the RF fingerprinting technique and on machine learning processing,has been tested experimentally and has achieved interesting performances.
文摘High-precision indoor positioning offers valuable information support for various services such as patient monitoring,equipment scheduling management,and laboratory safety.A traditional indoor positioning technology,fingerprint indoor positioning,often employs the K-nearest neighbor(KNN)algorithm to identify the closest K reference points(RPs)via the received signal strength(RSS)for location prediction.However,RSS is susceptible to environmental interference,leading to the selection of RPs that are not physically the closest to the user.Moreover,using a fixed K value is not the optimal strategy.In this work,we propose a novel approach,the dynamic Knearest neighbor method based on strong access point(AP)credibility(SAPC-DKNN),for indoor positioning.In SAPC-DKNN,we leverage prior knowledge of RSS path loss and employ the RSS fluctuation area to quantify the significance of different APs.We integrate the similarity of AP sets within the range of strong APs and formulate a weighted distance metric for RSS based on the credibility of strong APs.Additionally,we introduce a dynamic K-value algorithm based on neighbor density(ND-DKA)for the automatic optimization of the K value for each test point.Experimental evaluations conducted on three datasets demonstrate that our method significantly reduces the average positioning error by 15.41%–64.74%compared to the state-of-the-art KNN methods.
