The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Takeshi Hattori - One of the best experts on this subject based on the ideXlab platform.
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a proposal of Location Estimation with maximum likelihood function using joint pdf of received signals and prior measured signals
Vehicular Technology Conference, 2006Co-Authors: K. Azuma, K. Matsumoto, Takeshi HattoriAbstract:A novel Location Estimation with Maximum Log-Likelihood Function using joint probability function (PDF) of Received Signals and prior Measured Signals is proposed in order to improve the accuracy of the Location Estimation of Mobile Station (MS). We assume a spatial correlation between the received signals and the prior measured signals in the vicinity of the measured points. Using Bayesian theorem, a Log-likelihood Function is derived based on a new conditional PDF associated with a correlation. Computer simulation is carried out to clarify the improvement of the accuracy of the Location Estimation. It is shown that the Estimation accuracy is improved by 25.4% with 16 prior measured points in 200 square meters as compared with that in case of conventional method.
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a new Location Estimation method based on maximum likelihood function in cellular systems
Vehicular Technology Conference, 2001Co-Authors: M Aso, M Kawabata, Takeshi HattoriAbstract:This paper presents a proposal of a new mobile station (MS) Location Estimation method based on the maximum likelihood function in cellular systems. This method is applied to signal strength, time of arrival (TOA), or time difference of arrival (TDOA) as measurements for Location Estimation. We evaluate the performance of this new method through simulations under various conditions in case signal strength is measured, including the comparison of the conventional methods.
Pramod K Varshney - One of the best experts on this subject based on the ideXlab platform.
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Location Estimation of a random signal source based on correlated sensor observations
IEEE Transactions on Signal Processing, 2011Co-Authors: Ashok Sundaresan, Pramod K VarshneyAbstract:The problem of Location Estimation of a source of random signals using a network of sensors is considered. A novel maximum-likelihood Estimation (MLE) based approach using copula functions is proposed. The measurements received at the sensors are often spatially correlated and characterized by a multivariate distribution. Using the theory of copulas, the joint parametric density of sensor observations (joint likelihood) is approximated assuming only the knowledge of the marginal likelihood functions of the sensor observations. The problem of selecting the best copula function to model the joint likelihood is approached as one of model selection and a model fusion strategy is used to reduce the effect of selection bias. An example involving source localization of a Poisson source is presented to illustrate the proposed approach and demonstrate its performance.
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target Location Estimation in sensor networks with quantized data
IEEE Transactions on Signal Processing, 2006Co-Authors: Ruixin Niu, Pramod K VarshneyAbstract:A signal intensity based maximum-likelihood (ML) target Location estimator that uses quantized data is proposed for wireless sensor networks (WSNs). The signal intensity received at local sensors is assumed to be inversely proportional to the square of the distance from the target. The ML estimator and its corresponding Crameacuter-Rao lower bound (CRLB) are derived. Simulation results show that this estimator is much more accurate than the heuristic weighted average methods, and it can reach the CRLB even with a relatively small amount of data. In addition, the optimal design method for quantization thresholds, as well as two heuristic design methods, are presented. The heuristic design methods, which require minimum prior information about the system, prove to be very robust under various situations
Chaolin Chen - One of the best experts on this subject based on the ideXlab platform.
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wireless Location tracking algorithms for environments with insufficient signal sources
IEEE Transactions on Mobile Computing, 2009Co-Authors: Pohsuan Tseng, Kaiten Feng, Yuchiun Lin, Chaolin ChenAbstract:Location Estimation and tracking for the mobile devices have attracted a significant amount of attention in recent years. The network-based Location Estimation schemes have been widely adopted based on the radio signals between the mobile device and the base stations. The Location estimators associated with the Kalman filtering techniques are exploited to both acquire Location Estimation and trajectory tracking for the mobile devices. However, most of the existing schemes become inapplicable for Location tracking due to the deficiency of signal sources. In this paper, two predictive Location tracking algorithms are proposed to alleviate this problem. The predictive Location tracking (PLT) scheme utilizes the predictive information obtained from the Kalman filter in order to provide the additional signal inputs for the Location estimator. Furthermore, the geometric-assisted PLT (GPLT) scheme incorporates the geometric dilution of precision (GDOP) information into the algorithm design. Persistent accuracy for Location tracking can be achieved by adopting the proposed GPLT scheme, especially with inadequate signal sources. Numerical results demonstrate that the GPLT algorithm can achieve better precision in comparison with other network-based Location tracking schemes.
River Lee - One of the best experts on this subject based on the ideXlab platform.
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Real-time indoor positioning system based on RFID heron-bilateration Location Estimation and IMU inertial-navigation Location Estimation
Proceedings - International Computer Software and Applications Conference, 2015Co-Authors: Chian C. Ho, River LeeAbstract:A real-time indoor positioning system is developed, featuring 2 novel perfectly complementary positioning methods: 1) RFID Heron-bilateration Location Estimation, based on external RFID infrastructure, and 2) IMU inertial-navigation Location Estimation, based on internal IMU module. At first, 2 or multiples of 2 active RFID tags as infrastructure landmarks are deployed along the surrounding walls in a single indoor space or room. After the infrastructure landmarks are set up, the handheld indoor positioning device begins to connect to the Bluetooth-based RFID reader by pairing with Bluetooth ID of the RFID reader. Then, on the screen of the handheld indoor positioning device, red landmarks on the 2D indoor map represents pre-deployed active RFID tags. When moving, the targeted handheld indoor positioning device keeps estimating the relative Location through external RFID infrastructure and keeps reckoning the inertial navigation through internal IMU module. Experimental results show these two proposed positioning methods, RFID Heron-bilateration Location Estimation and IMU inertial-navigation Location Estimation can cooperatively improve the accuracy and reliability of indoor positioning system further. Finally, the screen of the handheld indoor positioning device can show the Location and orientation indications of the targeted user on the 2D indoor map accurately and immediately.
Chian C. Ho - One of the best experts on this subject based on the ideXlab platform.
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Real-time indoor positioning system based on RFID heron-bilateration Location Estimation and IMU inertial-navigation Location Estimation
Proceedings - International Computer Software and Applications Conference, 2015Co-Authors: Chian C. Ho, River LeeAbstract:A real-time indoor positioning system is developed, featuring 2 novel perfectly complementary positioning methods: 1) RFID Heron-bilateration Location Estimation, based on external RFID infrastructure, and 2) IMU inertial-navigation Location Estimation, based on internal IMU module. At first, 2 or multiples of 2 active RFID tags as infrastructure landmarks are deployed along the surrounding walls in a single indoor space or room. After the infrastructure landmarks are set up, the handheld indoor positioning device begins to connect to the Bluetooth-based RFID reader by pairing with Bluetooth ID of the RFID reader. Then, on the screen of the handheld indoor positioning device, red landmarks on the 2D indoor map represents pre-deployed active RFID tags. When moving, the targeted handheld indoor positioning device keeps estimating the relative Location through external RFID infrastructure and keeps reckoning the inertial navigation through internal IMU module. Experimental results show these two proposed positioning methods, RFID Heron-bilateration Location Estimation and IMU inertial-navigation Location Estimation can cooperatively improve the accuracy and reliability of indoor positioning system further. Finally, the screen of the handheld indoor positioning device can show the Location and orientation indications of the targeted user on the 2D indoor map accurately and immediately.
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Real-time RFID indoor positioning system based on kalman-filter drift removal and heron-bilateration Location Estimation
IEEE Transactions on Instrumentation and Measurement, 2015Co-Authors: Chung-hao Huang, Chian C. Ho, Lang Long Wu, Lun-hui Lee, Zu Hao LaiAbstract:This paper proposes Kalman-filter drift removal (DR) and Heron-bilateration Location Estimation (LE) to significantly reduce the received signal strength index (RSSI) drift, localization error, computational complexity, and deployment cost of conventional radio frequency identification (RFID) indoor positioning systems without any sacrifice of localization granularity and accuracy. By means of only one portable RFID reader as the targeted device and only one pair of active RFID tags as the border-deployed landmarks, this paper develops a real-time portable RFID indoor positioning device and cost-effective scalable RFID indoor positioning infrastructure, based on Kalman-filter DR, Heron-bilateration LE, and four novel preprocessing/postprocessing techniques. Experimental results reveal that the proposed Kalman-filter DR method is faster and better to converge the distance measurement (DM) error than conventional probability/statistics in terms of various relative distances under certain RSSI drift effect condition, and the proposed Heron-bilateration LE method is also faster and better to converge the LE error than conventional proximity pattern matching and trilateration in terms of three or more landmarks under certain DM error condition. On the other hand, a portable RFID indoor positioning device is smoothly implemented on an Android smartphone platform attached with a portable Bluetooth-based RFID reader.
