The availability of a ubiquitous, ever-available positioning system would allow the development of new opportunities and business models, creating innovative products and the delivery of highly personalized services.
Location-aware applications are finally on the brink of revolutionizing the way in which people go about their business on a daily basis. Of the top 50 Android applications chosen by Google, about 1 in 7 was built using the GPS functionality. Nokia has developed GPS-enabled handsets, such as the N95, and navigation solution packages. Continuous lines refer to the ML estimators here proposed. Dashed lines refer to other approaches from the literature.
As it is possible to notice, at high SNR all the ML estimators outperform the approaches proposed in the literature, regardless the adoption of phase-only i. Similar performance is given by 16 ; in this case, the small degradation is due to the model mismatch, since the latter was derived from the assumption of constant amplitude signal, which does not correspond to the simulated scenario.
A small performance degradation is offered by estimator 17 , which does not require the availability of the signal amplitude then leading to a simpler implementation. At high SNR, 3 dB of performance gap is present between 14 i. Comparison between ML estimators and other approaches presented in [ 14 , 19 ] shows the enhanced performance of the first group. In general, it is possible to notice the classical behavior of non-linear estimators, with different operating regions at different SNR [ 34 , 35 ].
At low SNR, the performance becomes quickly quite poor for all the estimators. In this region, approach 18 has a performance slightly improved with respect to the ML estimators. The explanation of such a phenomenon can be found in the structure of the functions that are maximized. Differently, the function 18 is depicted in Fig. It is possible to notice the sharpness of the main lobe of 14 , for which the ML is expected to work very well in the high SNR region, where the probability of selecting the correct lobe is high. The situation swaps at low SNR, where the noise could lead to ambiguities in the selection of the main lobe of 14 ; in this case a smoother behavior, as reported in Fig.
Localization RMSE as a function of the tag position along the x axis, for various estimator structures. It is possible to notice that the derived ML outperforms the other approach for every tag position at the selected SNR of 15 dB. Depending on the tag position, up to one order of magnitude of difference in the localization accuracy is experienced e.
Differently, when the tag is located along the direction orthogonal to the middle point of the synthetic aperture described by the reader, the two approaches presents the same performance. Notice that uniform spatial samples along the synthetic aperture where considered in the simulation. The derivation was conducted under different models of the received signal, in order to obtain estimators exploiting only the phase information, or both amplitude and phase. The derived estimators were compared with the approaches already proposed in the literature, showing the performance improvement that can be obtained using the proposed signal processing scheme.
Moreover, it has been proved that other approaches presented in the literature lead to the same estimation structure, then presenting equivalent performance.
It has been shown how approaches that properly weights the phase information with the received signal amplitude ensure the best performance. In the high SNR regime, the use of the ML estimator corresponds to an improvement of up to one order of magnitude in the estimation error for certain tag positions. Differently, at low SNR the performance of all the estimators is similar and ambiguities limit severely the effectiveness of these positioning schemes.
Future research attention should be devoted in determining the design criteria of these positioning schemes, since many aspects affect the feasibility of the localization and the performance, as the number of measurements and their position i. Finally, the characterization of the effects of multipath propagation, which can severely affect the phase information, should be investigated, also considering experimental studies in real scenarios.
Additional phase shifts due to the circuits at reader and tag side [ 13 ] are supposed calibrated out.
Intersections of circles denote the possible locations of the tag considering the two phase measurements first 20 displayed. Notation s i p , a i denotes that the sample s i is a function of the tag position p , to be estimated, and of the unknown parameter a i.
takomarova.ru/wp-content/436/coqef-chastnie-znakomstva-v.php Examples of objective functions for the ML estimator 14 and the approach presented in [ 19 ]. The author would like to thank F. Guidi for the fruitful discussions and carefully reading the manuscript, and the anonymous reviewers for the constructive comments provided. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Skip to main content Skip to sections. Advertisement Hide. Download PDF. On phase-based localization with narrowband backscatter signals. Open Access. First Online: 12 November Part of the following topical collections: Network Localization.
In order to enable precise localization also with narrowband RFID, the exploitation of phase measurements from the backscatter signal has been proposed [ 13 ]. In particular, due to the backscattering mechanism, the phase difference between the transmitted signal and the received tag response is the object of the measure taken for localization.
Due to the intrinsic periodicity of the phase of a narrowband signal, leading to ambiguities in distance and hence location estimation, multiple phase measurements are always considered for positioning [ 13 ]. Different approaches differ on the method in which this set of phase measures is collected. More specifically, there is a class of problems dealing with localization of a reader or a tag using multiple phase measurements. In particular, we can consider the localization of the following: A moving tag using a steady reader [ 14 , 15 , 16 ], A steady tag using a moving reader [ 17 , 18 , 19 , 20 , 21 , 22 ], A moving reader e.
The main contributions of this paper are as follows: The analysis of the phase-based localization technique for narrowband RFID, by presenting several examples, insights and practical configurations; The derivation of the ML estimator structure for the considered problem; The comparison of the proposed signal processing scheme with others proposed in the literature, showing their performance for the localization of a tag with a moving reader, and the discussion of the different approaches. After traveling to the tag at distance d T from the transmitting antenna, the signal is backscattered and reaches the antenna of the receiver, placed at distance d R from the tag see Fig.
According to this configuration, the transmitting antenna, the tag and the receiving antenna forms a bi-static pair 1. Moreover, each interrogation can be performed at a given radio channel of frequency f i. Open image in new window. A practical example is reported in Fig.
Such a movement describes the so-called aperture , in relation with the synthetic aperture radar SAR techniques. In Fig. During the movement, N phase measures i. Starting from these phase samples, the position of the tag is estimated, with a proper signal processing scheme. It is then evident how the number of samples i. In fact, the position of reader where samples are taken impacts in two different ways the localization results: 1. It affects the capability of localizing the tag by solving the phase ambiguities, as described in Section 2.
Increasingly, satellite and terrestrial positioning techniques are being combined for maximum performance; to produce the next wave of location-based devices and services, engineers need to combine both components. Various Anonymous Naval Personnel. Differently, when the tag is located along the direction orthogonal to the middle point of the synthetic aperture described by the reader, the two approaches presents the same performance. At the top of this web page is a link to "Link Budgets index page". Terrestrial Network-Based Positioning and Navigation 3. The standard voltage is V and the frequency is 50 Hz.
The estimator previously derived exploits the measurements of both phase and amplitude taken on the received signal. If an estimator structure resorting to phase measurements only wants to be obtained, as often considered in the literature, a simplified model assuming constant amplitude of the received signal can be considered.
Differently, [ 14 ] proposed to consider phase-difference values. In particular, the results obtained with the ML estimators are reported, together with the results for approach 18 , for both amplitude and phase or phase-only i. In order to easy the reading of the figures, the legends report the equation related to a specific approach together with indication MRC for estimators 14 , 16 , and 18 , which use the received signal amplitude to weight the phase terms, or with indication EGC for estimators 17 and 23 exploiting only the phase information.
With Fig. For comparison purposes, in Fig. Again, it is possible to appreciate the increased accuracy offered by the ML 16 with respect to approach 18 and Moreover, approximation 17 is showed to hold, especially in the asymptotic region, thanks to the supposed constant amplitude in the model adopted. In this case, estimators accounting for variable amplitude show a degraded performance due to the model mismatch, so that the use of the phase information only is beneficial, especially at medium SNR, due to the absence of any variation in the amplitude of the received signal ideal case.
It is interesting to see that the presence of a signal with constant amplitude for all the samples does not improve significantly the estimation accuracy which can be obtained in the asymptotic region, but moves significantly the SNR value where such a region starts. In fact, for the ML estimator 16 , the asymptotic region starts at 9 dB SNR in case of signal with constant amplitude, while 16 dB SNR are necessary in case the signal path-loss for every phase sample is considered in the simulation. Previous results presented the performance of the different estimators for a tag placed in a specific position.
In order to show how the tag position impacts its position estimation capability with phase-based techniques, Fig. In particular, the ML 16 and 18 are considered. Transmitting and receiving antenna can be also co-located as in classical RFID. Advantages of bi-static configurations for RFID have been recently highlighted [ 37 , 38 ]. Notice that, according to the approaches proposed in the following, ambiguity is not resolved explicitly as in other approaches [ 39 ], but every new measurement poses additional constraints to the problem, then making feasible the position estimation.
Acknowledgements The author would like to thank F.
Availability of data and materials Not applicable. Competing interests The author declares that he has no competing interests. Wiley, Google Scholar. Costanzo, D. Masotti, T. Ussmueller, R. IEEE Microw. CrossRef Google Scholar. AUB2 en. Method and apparatus for improved location determination in a private radio network using a public network system. USB2 en.
Combines satellite and terrestrial positioning techniques, using a signal processing approach. Discusses the applicability of developed. Satellite and Terrestrial Radio Positioning Techniques. A signal processing perspective. Book • Edited by: Davide Dardari, Emanuela Falletti and Marco.
Position location using global positioning signals augmented by broadcast television signals. Differential correction system enhancement leverages roving receivers enabled for a non-GPS, secondary PN and T signal to characterize local errors.