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Optimizing Indoor localization and tracking: An Energy-Efficient Approach Using Received Signal Strength and Mixstyle Neural Networks With Implicit Unscented Particle Filtering

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第7期38卷

作者： Shanthi, C. Porselvi, R. Reddy, A. Basi Ganesan, S. Anna Univ Dept Instrumentat Engn MIT Campus Chennai Tamil Nadu India Tagore Engn Coll Dept Elect & Commun Engn Chennai Tamil Nadu India Mohan Babu Univ Sch Comp Dept Comp Sci & Engn Tirupati Andhra Prades India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Aeronaut Engn Chennai Tamil Nadu India

In indoor environments, the unpredictable noise in received signal strength indicator (RSSI) measurements causes very high estimation errors for target localization. Nowadays, RSSI-based localization systems are widely used to deal with higher noise levels in RSSI measurements and to assure more accuracy in target localization. In this paper, Optimizing Indoor localization and tracking: An Energy-Efficient Approach Using Received Signal Strength and Mixstyle Neural Networks with Implicit Unscented Particle Filtering (OILT-MNN-IUPF) is proposed. The proposed method consists of two range-free target localization schemes in wireless sensor networks (WSN) for an indoor setup: (i) mixstyle neural network (MNN) used for regression tasks and (ii) fusion of MNN and the implicit unscented particle filter (IUPF). The fusion-based model is named the MNN + IUPF approach. There is no need to compute distances using field measurements for the proposed localization solutions, here three RSSI measurements to trace the mobile target. Also, this paper discusses the energy consumption related to the typical trilateration and MNN-based target localization. With the proposed MNN-based schemes, linear, sigmoid, RBF, and polynomial are the four kernel functions estimated on the accuracy of target localization. The proposed OILT-MNN-IUPF model achieves 25.05%, 20.17%, and 23.19% lower average localization error and 23.11%, 20.11%, and 24.09% less root mean square error compared with existing models.

关键词： energy efficient implicit unscented particle filter localization and tracking mixstyle neural network received signal strength indicator

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Super-resolution localization and tracking in WiFi Sensing 33

Super-resolution Localization and Tracking in WiFi Sensing

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33rd IEEE International Conference on Computer Communications and Networks (ICCCN)

作者： Wang, Jian Chuang, Jack Sempert, Sebastian Golmie, Nada NIST Wireless Networks Div Gaithersburg MD 20899 USA Tech Univ Ilmenau Inst Informat Technol Ilmenau Germany

ISBN: (纸本)9798350348439;9798350384611

Integrated sensing and communication (ISAC) systems have been investigated by the research and standardization communities in the recent past. Accurately localizing the target and tracking the target's movement are critical for numerous smart Internet of Things (IoT) systems (smart manufacturing, smart transportation, etc.). This paper aims to realize super-resolution localization and tracking in WiFi sensing by leveraging the IEEE 802.11ad beamforming training procedure. We leverage the CLEAN-Space-Alternating Generalized Expectation-maximization (CLEAN-SAGE) algorithm on a single beam sweeping cycle for target localization and investigate the targets' delays and angle estimation. For tracking moving targets, we design mechanisms to estimate the target's motion, including the target's velocity and motion pattern, such as estimating the target's spatial positions over time to obtain the Doppler shift or tracking its trajectory using a Kalman filter. In order to prove that our approach works effectively, we conduct a thorough performance evaluation study. Our evaluation results confirm that the CLEAN-SAGE algorithm can achieve estimation performance beyond the ISAC system's inherent bandwidth and beamwidth constraints. Furthermore, we provide insights into how system configurations, including antenna size, beam overlap, and the number of iterations in the SAGE algorithm, influence its performance.

关键词： Edge processing IoT applications mmWave CLEAN and SAGE WiFi sensing Super-resolution algorithm localization and tracking

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Smart Antenna System for Electronic Toll Collection with Vehicle localization and tracking using End Users' Smartphones with BLE 9

Smart Antenna System for Electronic Toll Collection with Veh...

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9th International Conference on Smart and Sustainable Technologies (SpliTech)

作者： Gomez-Tornero, Jose-Luis Rabadan-Parra, Alejandro Cavero-Herranz, Victor Gil-Martinez, Alejandro Canete-Rebenaque, David Rojo-Fernandez, Javier Tech Univ Cartagena Dept Informat & Commun Technol Cartagena Spain INDRA Sistemas SA Dept Tolling Solut Madrid Spain

ISBN: (纸本)9798350390797;9789532901351

In this paper, a two-dimensional amplitude-monopulse smart antenna system is proposed to identify and localize smartphones connected with the Bluetooth Low Energy (BLE) wireless standard, and being carried inside automobiles. The systems is applied for Electronic Toll Collection (ETC) of vehicles. The antenna topology is described and preliminary experimental results are reported, demonstrating the capacity to track vehicles in real time with low mean error and a spatial resolution below 1 meter, covering an area of 8 m x 8 m below the smart antenna system mounted on a 4.7 meter high ETC gateway. These performances seems to be sufficient to accurately distinguish between two adjacent lanes and to resolve standard vehicles sizes in two dimensions and real time, both in stop-and-go and free-toll operation modes. The proposed ETC architecture avoids the need of extra automated radio transponder devices, since the users' own smartphone running the widespread BLE applications is sufficient, thus reducing the implementation cost, maintenance and scalability.

关键词： Electronic Toll Collection (ETC) Bluetooth Low Energy (BLE) RSSI-based real-time RFID localization and tracking smart antenna system amplitude monopulse antenna

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Random Finite Set Approach to Signal Strength Based Passive localization and tracking

Random Finite Set Approach to Signal Strength Based Passive ...

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IEEE/ION Position, Location and Navigation Symposium (PLANS)

作者： Kaltiokallio, Ossi Yigitler, Huseyin Talvitie, Jukka Valkama, Mikko Tampere Univ Unit Elect Engn Tampere Finland Aalto Univ Dept Commun & Networking Espoo Finland

ISBN: (纸本)9781665417723

Radio frequency sensor networks can be utilized for locating and tracking people within coverage area of the network. The technology is based on the fact that humans alter properties of the wireless propagation channel which is observed in the channel estimates, enabling tracking without requiring people to carry any sensor, tag or device. Considerable efforts have been made to model the human induced perturbations to the channel and develop flexible models that adapt to the unique propagation environment to which the network is deployed in. This paper proposes a noteworthy conceptual shift in the design of passive localization and tracking systems as the focus is shifted from channel modeling to filter design. We approach the problem using random finite set theory enabling us to model detections, missed detections, false alarms and unknown data association in a rigorous manner. The Bayesian filtering recursion applied with random finite sets is presented and a computationally tractable Gaussian sum filter is developed. The development efforts of the paper are validated using experimental data and the results imply that the proposed approach can decrease the tracking error up to 48% with respect to a benchmark solution.

关键词： Received signal strength RF sensor network localization and tracking random finite set Gaussian sum filter

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Bayesian Joint localization and tracking Algorithm Using Multiple-Input Multiple-Output Radar 9

Bayesian Joint Localization and Tracking Algorithm Using Mul...

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9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： Westerkam, Anders Malthe Manchon, Carles Navarro Mogensen, Preben Pedersen, Troels Aalborg Univ Aalborg Denmark

ISBN: (纸本)9798350344523

We present a novel joint localization and tracking algorithm for multiple-input multiple-output active radars. The proposed algorithm, which we dub Bayesian localization and tracking (BLaT), relies on approximate Bayesian inference using the mean field approach and processes all available received data to jointly estimate the target's track and location. This approach makes it possible to take advantage of the inherent synergy between the tracking and localization tasks. BLaT is shown by simulation to outperform a classical sequential processing baseline in terms of its ability to track targets in low signal-to-noise ratio conditions as well as superior tracking of manoeuvring targets.

关键词： Bayesian Learning localization and tracking Active Sensing MIMO-radar

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Simultaneous localization and tracking for UAV-Enhanced Positioning Network 98

Simultaneous Localization and Tracking for UAV-Enhanced Posi...

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98th IEEE Vehicular Technology Conference (VTC-Fall)

作者： Liang, Tianhao Zhang, Tingting Harbin Inst Technol Sch Elect & Informat Engn Shenzhen Peoples R China

ISBN: (纸本)9798350329285

The traditional Global Navigation Satellite System (GNSS)-based localization and tracking method will lead to a notable performance reduction when the navigation signals are obstructed. In this paper, by utilizing the inherent benefits of UAVs, such as reliable line-of-sight (LoS) propagation and exceptional mobility, we employ the UAVs to achieve the seamless localization and tracking. Specifically, we first analytically derive distribution of noise for the vehicle-to-UAV (V2U) measurements. Subsequently, we provide a general distributed simultaneous localization and tracking (SLAT) method of the UAV involved network via non-parametric belief propagation (NBP) algorithm, where the UAVs and vehicles are located and tracked jointly. Numerical simulations shed light on the benefits of our proposed method in terms of localization and tracking accuracy.

关键词： UAV localization and tracking non-parametric belief propagation

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Target localization and tracking based on improved Bayesian enhanced least-squares algorithm in wireless sensor networks

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COMPUTER NETWORKS 2020年第0期167卷 106968-000页

作者： Wang, Tao Wang, Xiang Shi, Wei Zhao, Zongmin He, Zhenxue Xia, Tongsheng Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Beijing 100101 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Hebei Agr Univ Sch Informat Sci & Technol Baoding 071001 Peoples R China

Classical tracking algorithms, such as the Bayesian algorithm, extended Kalman filter (EKF), and classical least-square (CLS) algorithm, have been extensively implemented at target localization and tracking in wireless sensor networks (WSNs). In this paper, an enhanced least-square algorithm based on improved Bayesian was developed for moving target localization and tracking in WSNs. We apply an improved Bayesian algorithm to obtain a set of sub-range probability based on target predictive location, and forming a range joint probability matrix. The range joint probability matrix is only automatically updated when the WSN testbed is in a dormant state. Then, the weight of every measurement is calculated and normalized based on the range probability matrix. Finally, the correction value of the target prediction position is calculated according to the weighted least-square algorithm. The experimental results show that compared with EKF, the weighted K-nearest neighbor algorithm (WKNN), the position Kalman filter (PKF), the fingerprint Kalman filter (FKF), variational Bayesian adaptive Kalman filtering (VBAKF), dual factor enhanced VBAKF (EVBAKF), and variational Bayes expectation maximization (VBEM) algorithms, the proposed algorithm improves the positioning accuracy by 35%, 32%, 18%, and 13%, 9%, 6%, and 0.4% respectively. In addition, the proposed algorithm reduces the computational burden by more than 80 percent compared with the Bayesian algorithm. (C) 2019 Published by Elsevier B.V.

关键词： Wireless sensor networks localization and tracking Improved Bayesian enhanced least-squares Prediction position

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Real-time Vehicle localization and tracking Using Monocular Panomorph Panoramic Vision 24

Real-time Vehicle Localization and Tracking Using Monocular ...

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24th International Conference on Pattern Recognition (ICPR)

作者： Akdemir, B. Belbachir, A. N. Svendsen, L. M. Teknova AS Smart Instrumentat Dept Grimstad Norway

ISBN: (纸本)9781538637883

This paper presents a feasibility analysis of the ORB-SLAM [1] for real-time vehicle localization and tracking using a monocular visual camera providing 360 degrees panoramic views. This method described in [1] was initially designed and developed for conventional cameras, making use of a method for detection and tracking visual features and estimating the camera trajectory while reconstructing the environment. The accuracy of the tracking depends on the ability of this method to robustly detect and match sufficient visual features. This work aims to extend this method to large monocular round views using fish-eye-like cameras allowing an increase of visual features with the aim of improving localization robustness. The main challenge in using a standard fish-eye camera for generating panoramic views is the reduction of visual performance due to a potential higher distortion and lower spatial resolution compared to that using a standard camera lens. The objective of this research is to perform a feasibility analysis of a method combining a camera equipped with a panomorph lens to generate real-time panoramic views at minimal distortion and ORB-SLAM to robustly detect and track visual features for real-time camera localization and tracking. A quantitative evaluation is performed on a vehicle driving in an outdoor natural scene with the monocular panomorph camera mounted on-front and without any other additional sensors. The results with analysis and a concluding summary are included as well.

关键词： panomorph panoramic vision visual SLAM localization and tracking

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Monitoring of laser welding using source localization and tracking processing by microphone array

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期86卷 21-28页

作者： Luo, Zhen Liu, Weidong Wang, Zhengming Ao, Sansan Tianjin Univ Sch Mat Sci & Engn Tianjin Peoples R China

Microphone is subject to the interference of working acoustic sources from surroundings. It is often difficult to acquire useful acoustic signal from the hostile environment. This problem has become the obstacle to use the acoustic signal for monitoring laser welding process. To monitor the weld quality effectively, a novel monitoring strategy on source localization and tracking of laser source is investigated in this work. A plane microphone array system composed of eight microphones has been applied to capture the acoustic signal. A time delay recognition has been employed to source localization and tracking. These results reveal that this system with the processing method is capable of distinguishing welding defects in laser welding process. By visualizing the position of laser source, it can directly judge and distinguish the weld quality.

关键词： localization and tracking Microphone array Acoustic signal Laser welding Process monitoring

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Fault Tolerant localization and tracking of Multiple Sources in WSNs Using Binary Data

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2014年第6期13卷 1213-1227页

作者： Michaelides, Michalis P. Laoudias, Christos Panayiotou, Christos G. Cyprus Univ Technol Dept Elect Engn & Informat Technol Lemesos Cyprus Univ Cyprus KIOS Res Ctr Intelligent Syst & Networks Dept Elect & Comp Engn Nicosia Cyprus

This paper investigates the use of a Wireless Sensor Network for localizing and tracking multiple event sources (targets) using only binary data. Due to the simple nature of the sensor nodes, sensing can be tampered (accidentally or maliciously), resulting in a significant number of sensor nodes reporting erroneous observations. Therefore, it is essential that any event tracking algorithm used in Wireless Sensor Networks (WSNs) exhibits fault tolerant behavior in order to tolerate misbehaving nodes. The main contribution of this paper is the development and analysis of a low-complexity, distributed, real-time algorithm that uses the binary observations of the sensors for identifying, localizing, and tracking multiple targets in a fault tolerant way. Specifically, our results indicate that the proposed algorithm retains its performance in tracking accuracy in the presence of noise and faults, even when a large percentage of sensor nodes (25 percent) report erroneous observations.

关键词： Wireless sensor networks multiple sources localization and tracking binary data fault tolerance

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