scholarly journals Dynamic Adjustment of Weighted GCC-PHAT for Position Estimation in an Ultrasonic Local Positioning System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7051
Author(s):  
José Manuel Villadangos ◽  
Jesús Ureña ◽  
Juan Jesús García-Domínguez ◽  
Ana Jiménez-Martín ◽  
Álvaro Hernández ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Weighting Factor ◽  
Position Estimation ◽  
Correlation Technique ◽  
Time Of Arrival ◽  
Dynamic Adjustment ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Local Positioning System ◽  
Cross Correlation Technique

Ultrasonic local positioning systems (ULPS) have been brought to the attention of researchers as one of the possibilities that can be used for indoor localization. Acoustic systems combine a suitable trade-off between precision, ease of development, and cost. This work proposes a method for measuring the time of arrival of encoded emissions from a set of ultrasonic beacons, which are used to implement an accurate ULPS. This method uses the generalized cross-correlation technique with PHAT filter and weighting factor β (GCC-PHAT-β). To improve the performance of the GCC-PHAT-β in encoded emission detection, the employment is proposed of mixed-medium multiple-access techniques, based on code division and time division multiplexing of beacon emissions (CDMA and TDMA respectively), and to dynamically adjust the PHAT filter weighting factor. The receiver position is obtained by hyperbolic multilateration from the time differences of arrival (TDoA) between a reference beacon and the rest, thus avoiding the need for receiver synchronization. The results show how the dynamic adaptation of the weighting factor significantly reduces positioning errors from 20 cm to 2 cm in 80% of measurements. The simulated and real experiments prove that the proposed algorithms improve the performance of the ULPS in situations with lower signal-to-noise ratios (SNR) than 0 dB and in environments where the multipath effect makes it difficult to correctly detect the encoded ultrasonic emissions.

scholarly journals Joint clock synchronization and position estimation in time of arrival–based passive positioning systems

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771878689 ◽  
Author(s):  
Shenghong Li ◽  
Lingyun Lu ◽  
Mark Hedley ◽  
David Humphrey ◽  
Iain B Collings
Keyword(s):  
Clock Synchronization ◽  
Likelihood Estimation ◽  
Least Square Method ◽  
Position Estimation ◽  
Least Square ◽  
Time Of Arrival ◽  
Positioning Systems ◽  
Reference Node ◽  
Localization Scheme ◽  
Wireless Signal

A widely used scheme for target localization is to measure the time of arrival of a wireless signal emitted by a tag, which requires the clocks of the anchors (receivers at known locations) to be accurately synchronized. Conventional systems rely on transmissions from a timing reference node at a known location for clock synchronization and therefore are susceptible to reference node failure. In this article, we propose a novel localization scheme which jointly estimates anchor clock offsets and target positions. The system does not require timing reference nodes and is completely passive (non-intrusive). The positioning algorithm is formulated as a maximum likelihood estimation problem, which is solved efficiently using an iterative linear least square method. The Cramér–Rao lower bound of positioning error is also analyzed. It is shown that the performance of the proposed scheme improves with the number of targets in the system and approaches that of a system with perfectly synchronized anchors.

scholarly journals A Loose-Coupled Fusion of Inertial and UWB Assisted by a Decision-Making Algorithm for Localization of Emergency Responders

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1463 ◽  
Author(s):  
André G. Ferreira ◽  
Duarte Fernandes ◽  
André P. Catarino ◽  
Ana M. Rocha ◽  
João L. Monteiro
Keyword(s):  
Decision Making ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Time Of Arrival ◽  
Robust Solution ◽  
Localization Accuracy ◽  
Positioning Systems ◽  
Emergency Responders ◽  
Pedestrian Dead Reckoning ◽  
Non Line Of Sight

Combining different technologies is gaining significant popularity among researchers and industry for the development of indoor positioning systems (IPSs). These hybrid IPSs emerge as a robust solution for indoor localization as the drawbacks of each technology can be mitigated or even eliminated by using complementary technologies. However, fusing position estimates from different technologies is still very challenging and, therefore, a hot research topic. In this work, we pose fusing the ultrawideband (UWB) position estimates with the estimates provided by a pedestrian dead reckoning (PDR) by using a Kalman filter. To improve the IPS accuracy, a decision-making algorithm was developed that aims to assess the usability of UWB measurements based on the identification of non-line-of-sight (NLOS) conditions. Three different data fusion algorithms are tested, based on three different time-of-arrival positioning algorithms, and experimental results show a localization accuracy of below 1.5 m for a 99th percentile.

scholarly journals Accuracy Bounds for Array-Based Positioning in Dense Multipath Channels

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4249 ◽  
Author(s):  
Thomas Wilding ◽  
Stefan Grebien ◽  
Ulrich Mühlmann ◽  
Klaus Witrisal
Keyword(s):  
Error Bound ◽  
Antenna Arrays ◽  
Estimation Error ◽  
Position Estimation ◽  
Position Error ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Multipath Interference ◽  
Positioning Systems ◽  
Performance Loss

The accuracy of radio-based positioning systems will be limited by multipath interference in realistic application scenarios. This paper derives closed-form expressions for the Cramér–Rao lower bound (CRLB) on the achievable time-of-arrival (ToA) and angle-of-arrival (AoA) estimation-error variances, considering the presence of multipath radio channels, and extends these results to position estimation. The derivations are based on channel models comprising deterministic, specular multipath components as well as stochastic, diffuse/dense multipath. The derived CRLBs thus allow an evaluation of the influence of channel parameters, the geometric configuration of the environment, and system parameters such as signal bandwidth and array geometry. Our results quantify how the ToA and AoA accuracies decrease when the signal bandwidth is reduced, because more multipath will then interfere with the useful LoS component. Antenna arrays can (partly) compensate this performance loss, exploiting diversity among the multipath interference. For example, the AoA accuracy with a 16-element linear array at 1 MHz bandwidth is similar to a two-element array at 1 GHz , in the magnitude order of one degree. The ToA accuracy, on the other hand, still scales by a factor of 100 from the cm-regime to the m-regime because of the dominating influence of the signal bandwidth. The position error bound shows the relationship between the range and angle information under realistic indoor channel conditions and their different scaling behaviors as a function of the anchor–agent placement. Specular multipath components have a maximum detrimental influence near the walls. It is shown for an L-shaped room that a fairly even distribution of the position error bound can be achieved throughout the environment, using two anchors equipped with 2 × 2 -array antennas. The accuracy limit due to multipath increases from the 1–10-cm-range at 1 GHz bandwidth to the 0.5–1-m-range at 100 MHz .

scholarly journals A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019 ◽  
Vol 9 (20) ◽  
pp. 4379 ◽  
Author(s):  
Alwin Poulose ◽  
Jihun Kim ◽  
Dong Seog Han
Keyword(s):  
Sensor Fusion ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Position Estimation ◽  
Sensor Data ◽  
Measurement Unit ◽  
Localization Error ◽  
Positioning Errors ◽  
Indoor Scenarios ◽  
Fusion Framework

Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

scholarly journals Robust Trilateration Based Algorithm for Indoor Positioning Systems

2021 ◽  
Vol 47 (3) ◽  
pp. 1195-1210
Author(s):  
Simeon Pande ◽  
Kwame S Ibwe
Keyword(s):  
Indoor Environment ◽  
Path Loss ◽  
Indoor Positioning ◽  
Position Estimation ◽  
Received Signal Strength Indicator ◽  
Position Information ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Distance Relationship ◽  
Indoor Positioning System

Abstract Indoor Positioning Systems (IPS) plays crucial roles in indoor environment items positioning used in self-navigating robots and helping hands. To obtain position information, positioning algorithms employing Received Signal Strength Indicator (RSSI) are of great benefits since they reuse the existing radio wireless infrastructures for indoor positioning. However, the changes in the indoor environment decrease the overall accuracy of the developed indoor positioning algorithms. To cope with the challenge of environmental dependency in indoor positioning, a robust algorithm using radio signal identification was developed. The algorithm uses circle expansion and reduction mechanism to achieve better RSSI-Distance relationship. The distances from RSSI-Distance relationship are used in trilateration algorithm for position estimation. Experiments were performed to compare position accuracy of the basic RSSI-Based and the proposed algorithm. Simulation results showed that proposed algorithm showed less average positioning errors by 11.2066% and 3.7279% at path loss coefficients of 3.11 and 3.21, respectively compared to the existing algorithms. Likewise, the proposed algorithm showed 2.7282% increase in positioning error when environment was changed from that of path loss coefficient 3.11 to 3.21. The existing basic algorithms show error fluctuation of 10% with the same environment changes. Keywords: Indoor Positioning System; RFID; RSSI; Trilateration

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

scholarly journals DeepLocBox: Reliable Fingerprinting-Based Indoor Area Localization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2000
Author(s):  
Marius Laska ◽  
Jörg Blankenbach
Keyword(s):  
Wireless Local Area Network ◽  
Indoor Localization ◽  
Local Area Network ◽  
Success Probability ◽  
Local Area ◽  
Position Estimation ◽  
Location Based Services ◽  
Global Navigation Satellite Systems ◽  
Area Network ◽  
Floor Plan

Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user’s position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.

scholarly journals Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning

2021 ◽  
Vol 11 (15) ◽  
pp. 6805
Author(s):  
Khaoula Mannay ◽  
Jesús Ureña ◽  
Álvaro Hernández ◽  
José M. Villadangos ◽  
Mohsen Machhout ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Experimental Tests ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Final Position ◽  
Positioning Systems ◽  
Local Positioning System ◽  
Tightly Coupled ◽  
Position Estimate

Indoor positioning systems have become a feasible solution for the current development of multiple location-based services and applications. They often consist of deploying a certain set of beacons in the environment to create a coverage volume, wherein some receivers, such as robots, drones or smart devices, can move while estimating their own position. Their final accuracy and performance mainly depend on several factors: the workspace size and its nature, the technologies involved (Wi-Fi, ultrasound, light, RF), etc. This work evaluates a 3D ultrasonic local positioning system (3D-ULPS) based on three independent ULPSs installed at specific positions to cover almost all the workspace and position mobile ultrasonic receivers in the environment. Because the proposal deals with numerous ultrasonic emitters, it is possible to determine different time differences of arrival (TDOA) between them and the receiver. In that context, the selection of a suitable fusion method to merge all this information into a final position estimate is a key aspect of the proposal. A linear Kalman filter (LKF) and an adaptive Kalman filter (AKF) are proposed in that regard for a loosely coupled approach, where the positions obtained from each ULPS are merged together. On the other hand, as a tightly coupled method, an extended Kalman filter (EKF) is also applied to merge the raw measurements from all the ULPSs into a final position estimate. Simulations and experimental tests were carried out and validated both approaches, thus providing average errors in the centimetre range for the EKF version, in contrast to errors up to the meter range from the independent (not merged) ULPSs.

scholarly journals The emergence of the red sequence at z~2 seen through galaxy clustering in the UKIDSS UDS

2012 ◽  
Vol 8 (S295) ◽  
pp. 105-108
Author(s):  
William G. Hartley ◽  
Omar Almaini ◽  
Alice Mortlock ◽  
Chris Conselice ◽  
Keyword(s):  
Dark Matter ◽  
Cross Correlation ◽  
Near Infrared ◽  
Correlation Technique ◽  
Dark Matter Halos ◽  
Star Forming ◽  
Deep Survey ◽  
Cross Correlation Technique ◽  
Infrared Survey

AbstractWe use the UKIDSS Ultra-Deep Survey, the deepest degree-scale near-infrared survey to date, to investigate the clustering of star-forming and passive galaxies to z ~ 3.5. Our new measurements include the first determination of the clustering for passive galaxies at z > 2, which we achieve using a cross-correlation technique. We find that passive galaxies are the most strongly clustered, typically hosted by massive dark matter halos with Mhalo > 1013 M⊙ irrespective of redshift or stellar mass. Our findings are consistent with models in which a critical halo mass determines the transition from star-forming to passive galaxies.

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