RTLS FOR DUMMIES PDF

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Rtls For Dummies Pdf

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INTRODUCTION. What is RTLS? Real-Time Locating Systems (RTLS) are precise positioning systems that not only enable a user to identify and track many . PDF | Real-time locating systems (RTLS, also known as real-time location systems) have become an Download full-text PDF Malik A: RTLS For Dummies. traceability and documentation, the Real Time Location Systems (RTLS) concept is making its way RTLS For dummies, Wiley publishing, Indianapolis, ISBN raudone.info

But the nurses refused to comply with the system they did not wear the ID badges and therefore could not be tracked. As a result of this, the system was never used, hence the importance of educating users and addressing any privacy or other concerns they might have.

Discussion, practical recommendations and conclusions In healthcare facilities, RTLS can be used to locate portable assets and equipment, locate staff quickly and efficiently, and improve workflow. Hospital throughput of patients can be improved by ensuring the correct medical staff and equipment are in the correct place at the right time. It is important to keep in mind that when vendors are more knowledgeable than the people procuring anything complex, the potential for dissatisfaction is likely to be present.

Many of the companies providing products are several years from profitability, they are investing in building a sales and customer support infrastructure, and it is unlikely that all of them will survive to maturity.

The problem for the clients of such companies will be how to support legacy closed, proprietary systems if the vendors are no longer trading.

Real-Time Location System (RTLS)

To reduce such risks, clients should insist on procuring standards-based technologies that support open APIs [ 8 ]. Prospective clients should also ask vendors bidding for an RTLS installation to provide references from existing customers covering previously delivered work, particularly work of similar nature and requirements as the current job. Many IT information technology projects fail, particularly large ones; they are either abandoned prior to implementation due to cost overruns , or they do not achieve the required functional or business benefits.

RTLS installations bring in additional factors that may lead to project failure. The IT sector has an inbuilt expertise in protecting themselves from the consequences of project failure. The deliverables and system functionality of a proposed system are detailed in its functional specifications.

The outcome is defined in such a way that allows the project to be declared a success if it can be shown to have met its functional specifications, regardless of whether or not it has also met the requirements as sold and anticipated by the client.

One problem here is that vendors are experts in the sciences of the hardware and marketing; they hard promote their hardware because they consider that to be their differentiator in the marketplace. Clients, on the other hand, are usually downloading a solution to an operational problem rather than a mere hardware installation , and misunderstandings can arise in the fine, but often critical details.

RTLS systems are high involvement products, and typically the evaluation, selection and procurement team will consist of a of a stakeholder panel drawn from only the procuring organisation.

The panel will examine and evaluate the offers received in detail; often they quickly adopt the domain and terminology of the vendors.

Vendors usually provide information about radio type and frequency of transmission, received signal strength RSSI , triangulation, multiple paths, etc. Depending on the makeup of the selection panel, this may or may not be relevant information, because they may not be cognisant of the differences in the capabilities of products offered, due to minutiae.

Unfortunately, although the product details supplied by the vendors may be accurate, downloaders responsible for the procurement of RTLS for the first time may not be aware of the consequences of decisions based on minutiae provided by vendors. The choice of RTLS technology must be very carefully made. For both algorithms, the mean offset error x component was almost a half of z component. The cumulative distribution functions of average offset errors from tests of AoA-based localization method before and after correction along with the reference method are presented in Figure The presented plots show how the correction vector influences the average offset error.

In a case the AoA-based localization algorithm without correction, the range of the error is from 25 cm to 87 cm and the median of the average offset error is 55 cm. The correction vector significantly shifts the distribution function to the left to the range from 6. Experimental cumulative distribution functions of the average resultant offset error for LSs AB pair for three algorithms. The z offset error components before and after applying the correction vector at each spatial location sample are compared in Figure 22 , and the resultant location offset errors are shown in Figure It shows that the correction vector reduced the location offset errors at most of the examined positions, and the average offset error of these 36 locations is reduced However, the SD of mean offset error increased from 8.

Comparative bar plots of the average offset errors of the z components for AD pair. The presented data for AD LSs pair show that alike for the AB LSs pair, the z component of the offset error was the biggest, compared to x and y components.

However, the SD of this component is relatively high compared to the x and z components. The cumulative distribution functions of the average offset errors of the AoA-based localization method before and after correction along with the reference method results for the 36 spatial location samples are presented in Figure The presented distribution functions clearly illustrate how the correction vector influences the average offset error.

For the results from the AoA-based localization algorithm without the correction vector, the median of the average offset error is 60 cm, whereas the range of the error is from 42 cm to 82 cm. The correction vector significantly shifts the distribution function to the left and the median of average offset error is reduced Experimental cumulative distribution functions of the average resultant offset error for LSs AD pair.

The precision distribution maps demonstrate how the uncertainty increases along with the distance from LSs; the uncertainty is worst at the bottom corners of the side opposite to the side with the active LSs, which is summarized in Table 2.

The Table 2 points out that the best precision is identified near the active LSs. The simulation results indicate also how different deployment heights of active LSs influence the uncertainty maps. For instance, Figure 8 c shows that the location uncertainty near the floor is significantly lower under the LS D , which is located lower than the LS A. The reason for this phenomenon is that in this area, the volume of the common solid of the two crossing cones is relatively big since their axes referring to the paths of arrival could be almost coaxial.

However, the cones are not coaxial due to the established 50 cm localisation dead zone near the wall. To overcome a problem of bad uncertainty-precision near the wall, it can be suggested to arrange the active LSs at different heights. The verification of this observation by the experimental results confirms that the best localization precision is achieved for the LSs AB pair and consequently for AD pair, the average value of matching ratio for the 36 spatial location samples is The customization verification proves a positive influence of the correction vector on localization accuracy.

Also for the AB pair the ranges of the offset error at these 36 test points have been diminished of The customization results challenges the mean offset error value of the reference algorithm of 32 cm, and even its range limits of 8.

The figures clearly indicate improvement of the accuracy after applying the correction vector, which vitally moves the cumulative distribution curves towards the lower value of the offset error.

Considering both LS configurations, after applying the correction vector, a half of the test locations, the offset error is less than 30 cm and 22 cm for AB and AD pairs respectively, compared to 31 cm for the reference algorithm. One can see that the correction vector improves the accuracy of the AoA method in a way that it challenges the reference method.

Customization of UWB 3D-RTLS Based on the New Uncertainty Model of the AoA Ranging Technique

Similarities in shape of all cumulative distribution functions, including the reference one, can indirectly validate the presented approach. Due to its applicability and complexity, discovering a trade-off among different features of indoor localization systems working in a 3D environment is an important research subject.

The customization is based on the performance assessment in a given working environment. The results show a significant difference in precision, up to 7. Furthermore, the analysis indicates also a disadvantage of placing the active LSs at the same height. Moreover, the simulated precision distribution maps define the areas of the best and worst localization precision, in such a manner that the best performance is noticeably near to the active LSs and the worst are at the corners hindmost from these LSs.

The extrapolation algorithm allows finding the localisation estimate even in contaminated environments by using the principle of a distance between two skewed lines.

Simulation and physical experiment results confirm that the proposed simple extrapolation angular-based 3D localization algorithm ensures a good localization performance and challenges the advanced UWB-based RTLS algorithms. The proposed analytical geometrical model of the AoA localization method in a 3D indoor space was evaluated in the simulation environment of Matlab where the model was implemented. The proposed solution was verified by comparison of simulation and physical experiment results.

The quantitative verification, in a form of matching ratios confirms that the analytical model matches the real measurement with a high probability level.

To further enhance the RTLS performance, the research may consider a region-based correction vector method, which adjusts the correction vector to the region of workspace. The distance from the active LSs can be used as an adaptive factor of the correction vector. All authors made the great contribution to the work.

Bartosz Jachimczyk performed the experimental part, modelled, analysed data and reported the results. Damian Dziak contributed to the experimental part and result analysis. Wlodek J. Kulesza guided the whole research and supported the structure of the paper. National Center for Biotechnology Information , U.

Journal List Sensors Basel v. Sensors Basel. Published online Jan Kulesza 3. Bartosz Jachimczyk 1 BetterSolutions S. Author information Article notes Copyright and License information Disclaimer. Grunwaldzka , Gdansk, Poland. Received Sep 14; Accepted Jan This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution CC BY license http: This article has been cited by other articles in PMC.

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Real-time locating system

Figure 1. Accuracy The accuracy property expresses the capability to obtain the true value of a measurand [ 40 ]. Figure 2. Figure 3. Extrapolation of localization point from the distance of the askew lines. It may include the following sources: Figure 4. Figure 5. Illustration of uncertainty cones and the solid—common part of the two cones. Figure 6. Figure 7.

Figure 8. Figure 9. Figure Photo of the indoor environment with mounted LSs and the coordinate system origin. Experimental Verification The suitability of the proposed systematic approach to the uncertainty, in terms of accuracy and precision of the AoA-based 3D localization, was verified by physical experiments for AD and AB LS pairs.

Precision Model Verification To verify the localization precision model, the simulation results of the proposed geometrical model of AoA localization in a 3D indoor space were compared with the results of physical experiments. AB Pair Case Study The exemplary z offset error component, before and after applying the correction vector in each spatial location sample, is compared with the corresponding component from the reference system, see Figure Comparative bar plots of the average resultant offset error for AB pair.

AD Pair Case Study The z offset error components before and after applying the correction vector at each spatial location sample are compared in Figure 22 , and the resultant location offset errors are shown in Figure Comparative bar plots of the average resultant offset error for AD pair.

Conclusions and Future Work Due to its applicability and complexity, discovering a trade-off among different features of indoor localization systems working in a 3D environment is an important research subject. Abbreviations The following abbreviations are used in this manuscript: Author Contributions All authors made the great contribution to the work. Conflicts of Interest The authors declare no conflict of interest. References 1. Cho H. Maalek R. Accuracy assessment of Ultra-Wide Band technology in tracking static resources in indoor construction scenarios.

Lee H. Singh S. Kamel Boulos M. Real-time locating systems RTLS in healthcare: A condensed primer. Health Geogr. Fisher J. Evaluation of real-time location systems in their hospital contexts.

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Porto S. Localisation and identification performances of a real-time location system based on ultra wide band technology for monitoring and tracking dairy cow behaviour in a semi-open free-stall barn. Chantaweesomboon W. Malik A. RTLS for Dummies. Jachimczyk B. Deak G. A survey of active and passive indoor localisation systems. Kong Y. Robust localization over obstructed interferences for inbuilding wireless applications.

IEEE Trans. Lim H. Zero-Configuration, Robust Indoor Localization: Machaj J.

Dardari D. Wehs T. Selimis G. The measured location may appear entirely faulty. This is a generally result of simple operational models to compensate for the plurality of error sources. It proves impossible to serve proper location after ignoring the errors.

Real time is no registered branding and has no inherent quality. A variety of offers sails under this term. As motion causes location changes, inevitably the latency time to compute a new location may be dominant with regard to motion.

Either an RTLS system that requires waiting for new results is not worth the money or the operational concept that asks for faster location updates does not comply with the chosen systems approach.

Location will never be reported exactly , as the term real-time and the term precision directly contradict in aspects of measurement theory as well as the term precision and the term cost contradict in aspects of economy.

That is no exclusion of precision, but the limitations with higher speed are inevitable. Recognizing a reported location steadily apart from physical presence generally indicates the problem of insufficient over-determination and missing of visibility along at least one link from resident anchors to mobile transponders. Such effect is caused also by insufficient concepts to compensate for calibration needs. Noise from various sources has an erratic influence on stability of results.

The aim to provide a steady appearance increases the latency contradicting to real time requirements. As objects containing mass have limitations to jump, such effects are mostly beyond physical reality. Jumps of reported location not visible with the object itself generally indicate improper modeling with the location engine.

Such effect is caused by changing dominance of various secondary responses. Location of residing objects gets reported moving, as soon as the measures taken are biased by secondary path reflections with increasing weight over time. Such effect is caused by simple averaging and the effect indicates insufficient discrimination of first echoes.

These standards do not stipulate any special method of computing locations, nor the method of measuring locations. This may be defined in specifications for trilateration , triangulation or any hybrid approaches to trigonometric computing for planar or spherical models of a terrestrial area. So placing readers in utility rooms, near elevators and above doors between hospital wings or departments to track assets is not a problem".

In many applications it is very difficult and at the same time important to make a proper choice among various communication technologies e. Wrong design decision made at early stages can lead to catastrophic results for the system and a significant loss of money for fixing and redesign.

To solve this problem a special methodology for RTLS design space exploration was developed. It consists of such steps as modelling, requirements specification and verification into a single efficient process. From Wikipedia, the free encyclopedia. This article's tone or style may not reflect the encyclopedic tone used on Wikipedia. See Wikipedia's guide to writing better articles for suggestions.

February Learn how and when to remove this template message. This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed.Simulation results show that the calibration point becomes a demarcation point around which the pattern zones are specified.

The proposed customized RTLS method applies the fingerprinting technique based on the comparison of the measurements with the established pattern. Lim H.

Therefore, one of the possible enhancement approaches is to develop a measurement analytical model, which can facilitate the customization of the AoA ranging technique in different 3D environments. Dimond D: Therefore, the effective size of the workspace is only The simulation test environment TE2 corresponds to the workspace size of the physical experiment. An RTLS can be realised using various technologies, including light, camera vision, infrared IR , sound, ultrasound, Bluetooth, Wi-Fi, RFID radio frequency identification; RFID tags can be either active, with a small power supply to send out a signal covering a range of up to metres, or passive, with no power supply and activated by a scanning signal, which limits their range of detection to less than a metre , ZigBee [ 9 ], ultra-wideband UWB , GPS global positioning system and Cellular, among other technologies [ 1 , 10 , 11 ].

AB Pair Case Study The exemplary z offset error component, before and after applying the correction vector in each spatial location sample, is compared with the corresponding component from the reference system, see Figure

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