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The stationary signal-strength-difference (SSSD)-based positioning technique has been proposed as a cost-effective, low-complexity solution. The positioning accuracy with this technique, however, is limited, as a result of the large error in the distance. In this paper, we contribute a simple and efficient method for the accuracy improvement issues in the distance. Starting from the concept of the Global Positioning System (GPS) pseudorange (PD) equation, a complete set of distance difference (DD) is formulated. In addition, we then correct the error in the distance by going in the set of DD, based on the least-squares (LS) sense. The theoretical model is established and verified via experimental data gathered in an urban environment. Results clearly show that the proposed method performs well when encountering a large error in the distance. On top of this, we investigate the accuracy improvement issues in the location estimation and propose an iterative LS algorithm by using the corrected distances. The positioning accuracy is studied based on the analysis of different location errors, and the effect of geometric dilution of precision (GDOP) is also investigated. The improvement of the positioning accuracy is clearly demonstrated through the experimental data collected at 55 different test points with 25 133 samples, which is outstanding. Index Terms--Geometric dilution of precision (GDOP), Global Positioning System (GPS) pseudorange (PD) equation, least-squares (LS) sense, stationary signal-strength difference (SSSD).

Inspired by recent promotions of wireless location services and applications, this paper develops a simple model for detecting a mobile caller location with low implementation complexity and promising accuracy. The model that is described in this paper employs the geometrical transformation method with single propagation delay measurement. Depending on the number of available surrounding base transceiver stations (BTSs), a set of the pseudodistances and pseudodistance differences is provided through the geometrical distances between the home and multiple foreign BTSs, which is called the geometrical transformation method. Accordingly, the mobile caller location can be detected using the circular location technique and the hyperbolic location technique. The major benefit of the proposed model is that during a call to detect the mobile caller's location, the use of geometrical transformation allows us to overcome the location handover problem. A forcing handover in a Global System for Mobile Communications (GSM) network or a three-way soft handover in a Universal Mobile Telecommunications System (UMTS) network, for example, occurs when detecting the location when a standard model that is based on the measurement of the propagation delay is utilized. Comparing the standard model with the proposed model, the former needs at least three propagation delays that were simultaneously measured from at least three BTSs, whereas the latter needs only a single propagation delay that was measured from the home BTS. Moreover, in the case where we do not have a priori information of a non-line-of-sight (NLOS) error, a deterministic parameter that is related to the propagation delay is defined. This parameter is shown as limited a priori information for calibrating the coarse resolution in the proposed location model, which is caused by the NLOS error. To have significant findings, we conduct a practical experiment in an urban environment based on a 1.8-GHz commercial cellular network. In the five-BTS case where the location error is less than 300 m, the probability of location error that the proposed model with a bit period of 1/2 achieved is greater than 0.85. Although the performance of the proposed location model does not satisfy the U.S. Federal Communications Commission Enhanced 911 (FCC E-911) requirements, it is a good solution for the deployment of wireless location services and applications. Index Terms--Enhanced 911, forcing handover, Global System for Mobile Communications (GSM), non-line-of-sight (NLOS) error, propagation delay, pseudodistance, pseudodistance difference, three-way soft handover, Universal Mobile Telecommunications System (UMTS).

A proposal for hyperbolic location analysis based on the difference between pairs of received signal strength, called the stationary signal-strength difference (SSSD), has been made. Its location accuracy, in general, is limited because of two impairment factors: One factor is a high level of SSSD measurement error resulting from the shadowing, multiple-access interference, and nonline-of-sight effects; another factor is an error resulting from the estimation of the reference distance between the mobile subscriber and the serving base transceiver station (BTS), called the estimation error. Because of both factors, a larger bias error in the corresponding distance difference is expected, particularly when the SSSD levels are measured using standard handheld devices. This paper studies first the effects of bias error and then proposes an error correction method to decrease the larger bias error initially met. The method is based on the least square algorithm, which is simple, has a low computation burden, and gives noniterative and explicit solutions. Using the calibration data that are collected in a commercial cellular 900 MHz network, we analyze the estimation and SSSD measurement errors and demonstrate the performance merits of the proposed correction method. Field trial results show that our method achieves significant improvement in bias error compensation and, consequently, in location error mitigation. Specifically, for urban and rural environments with a six-BTS case, the mean error of location estimation can be reduced by an average of approximately 27% and 29%, respectively, reaching the lowest (best) values of 205 and 271 m, respectively. Index Terms--Hyperbolic location, least square (LS), multiple-access interference (MAI), nonline of sight (NLOS), received signal strength (RSS), shadowing, stationary signal-strength difference (SSSD).

Referring received signal strength (RSS) to a signal propagation model to find user location is one of the most promising strategies in wireless communications. This paper develops a simple method based on relative signal-strength measurements, that is, the differences in stationary signal strength measured at the user location from multiple base transceiver stations (BTSs). The stationary signal strength is the averaged RSS and also is a stationary Gaussian process. In using this method, it is vitally important to confirm that some uncertain propagation parameters can be canceled out while a signal propagation model is merged into our method for locating users. In this way, the differences in stationary signal strength lead to two solutions: One is the distance difference between pairs of BTSs, and the other is the distances from the user location to the multiple BTSs. Consequently, the hyperbolic positioning algorithm due to the distance-difference solution and the circular positioning algorithm due to the distances solution can be presented, respectively. Afterward, some experimental results were drawn from a field trial in a real propagation environment. Results show that the hyperbolic and circular positioning algorithms can locate the user to within about 350 and 300 m in 67 percentile, respectively. Compared with the numerical result reported in the literature on existing methods based on RSS only, our method is superior. Despite the result not meeting Federal Communications Commission (FCC) requirements, this method proved to be sufficiently simple and efficient in terms of the computation at burden and network signaling load. Index Terms--Circular positioning algorithm, hyperbolic positioning algorithm, stationary signal-strength difference.

High population density and the presence of many more motorcycles than cars make the noise environment in Taiwan different from that in other countries. There is growing concern about the electromagnetic effects within this environment. With the measured radio noise, the minimum suggested receive power in an urban environment ranges from 354 MHz to 426 MHz. We measured the properties of noise at an urban center, a nearby port, and a freeway exit, which are located in the same city, and on a hill lying adjacent to the city. We chose an urban center and a nearby hill as the noise environment for the following reasons: (a) The noise margin at urban areas is smaller than that at suburban and rural areas. (b) The coverage of the measurement on a hill is larger than that in a city. (c) The relation of the noise environment between a hill and an urban center can be obtained. The statistical distributions of the four particular noise environments are shown and design constraints for a broadcasting system are revealed. Index Terms--Electromagnetic interference, electromagnetic radiative interference, noise measurement, radio broadcasting, UHF radio propagation.

Data variability has long been a problem for radiated emission testings of electronic, telecommunication and information technology equipment. When a piece of electronic equipment undergoes electromagnetic interference (EMI) test, the inherent data variability is inevitable even with known test facility uncertainties. This study presents a methodology for analyzing this variability based on a modified analysis of variances (ANOVA), with polynomial regression analyses. The data variability is estimated after prototype has been built in a design laboratory. The analytical analysis results can also be used to provide confidence limits for internal in-process EMI control when the equipment is mass-produced. Index Terms--Analysis of variances (ANOVA), confidence level, design of statistical experiments, F distribution, factorial treatment designs, mixed models, polynomial regression analyses, statistical significance.