Received Signal Strength Distribution Research Articles

3-D Indoor Positioning Based on Passive Radio Frequency Signal Strength Distribution

In recent years, indoor positioning systems (IPSs) have received attention from many research fields, such as robotics, navigation, human-computer interaction, etc. However, IPS based on passive radio frequency (PRF) technology is still rare. This paper proposes an three-dimensional (3D) IPS based on received signal strength (RSS) distribution and Gaussian process regression (GPR). Traditional RSS-based positioning systems have a transmitter with known frequencies, while in the proposed PRf signal of Opportunity -3D IPS (PRO-3DIPS), the system neither deploys new transmitters nor uses any a priori knowledge of transmitters. Furthermore, PRO-3DIPS integrates multiple Signal of Opportunity (SoOP) sources, shadowing, fading, and also captures scenario signatures. Data collection and analysis of PRF-based RSS distribution in 3D space enables the capability of 3D positioning. Three methods are applied and compared to find the frequency band most impacted by the scenario to achieve the best positioning performance as well as used in the estimation of RSS distribution. The RSS distribution is estimated by measuring the PRF spectrum on a fixed grid in the scenario. Using the RSS distribution, the GPR can accurately locate the receiver position. RSS at 90-gridded positions were collected in the experiment scenario, with one hundred samples at each position. The experimental result shows that a root mean square error (RMSE) of the proposed PRO-3DIPS is 0.292 meters when the sampling distance is 1 meter. The result demonstrates that the PRF spectrum is a new modality for the positioning task, which demonstrates better performance than most existing RF-based technologies.

An Effective Fingerprint-Based Indoor Positioning Algorithm Based on Extreme Values

Wi-Fi-based fingerprint indoor positioning technology has gained special attention, but the development of this technology has been full of challenges such as positioning time cost and positioning accuracy. Therefore, selecting reasonable Wireless Access Points (APs) for positioning is essential, as the more APs used for positioning, the higher the online computation, energy and time cost. Furthermore, the received signal strength (RSS) is easily affected by diverse interference (obstacles, multipath effects, etc.), decreasing the positioning accuracy. AP selection and positioning algorithms are proposed in this paper to solve these issues. The proposed AP selection algorithm fuses RSS distribution and interval overlap degree to select a small number of APs with high importance for positioning. The proposed positioning algorithm uses the location distance between reference points (RPs) to construct a circle and leverages extreme values (maximum and minimum values) of circles to determine the possibility that the test point (TP) appears in each circle, then it finds useful APs to determine the weight of RPs. Extensive experiments are conducted in two different areas, and the results show the effectiveness of the proposed algorithm.

A Novel Indoor Fingerprint Localization System Based on Distance Metric Learning and AP Selection

A desirable fingerprint-based indoor localization (FIL) system aims to achieve an accurate positioning result within an acceptable time consumption, which is still challenging for application. Building a practical FIL system is a composite task of feature extraction and location estimation, resulting in related methods that is often hard to consider both the positioning accuracy and time consumption. This article proposes a novel FIL system that uses a combination of distance metric learning (DML) and access point (AP) selection method to tradeoff the positioning accuracy and time consumption. Specially, we first abstract the localization process to develop a mathematical model from the perspective of probability theory and reveal the significant impact of the received signal strength (RSS) similarity comparison on FIL. Then, we propose a perturbation theory-based AP selection method to select the best-position-discrimination AP subset from all to reduce the positioning time consumption. Meanwhile, we propose a DML-based method to extract the RSS distribution which involves the indoor environmental information, and further use it in RSS fingerprint similarity comparison to improve the positioning accuracy. We introduce the signal path-loss model into the proposed method for training to get the best similarity metric function. Finally, experimental results show that both the positioning accuracy and the time consumption are comparatively improved in the online phase by the proposed FIL system.

Factor Optimization for the Design of Indoor Positioning Systems Using a Probability-Based Algorithm

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They advertise useful information, such as the received signal strength (RSS), that is processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing the arrangement of APs over the environment using an enhanced probability-based algorithm. From the assumption that a log-distance path loss model can reasonably describe, on average, the distribution of RSS throughout the environment, we build a simulation framework to analyze the impact, on the accuracy, of the main factors that constitute the positioning algorithm, such as the number of reference points (RPs) and the number of samples of RSS collected per test point. To demonstrate the applicability of the proposed solution, a real-world testbed dataset is used for validation. The obtained results for accuracy show that the trends verified via simulation strongly correlate to the verified in the dataset processing when allied with an optimal configuration of APs. This indicates our method is capable of providing an optimal factor combination—through early simulations—for the design of more efficient IPSs that rely on a probability-based positioning algorithm.

Adaptive Density Graph-Based Manifold Alignment for Fingerprinting Indoor Localization

The received signal strength (RSS) fingerprint-based indoor localization has been considered as a promising solution, due to its relatively high localization accuracy and its ease of use in widespread Wireless Local Area Network (WLAN) infrastructure. A major bottleneck is that the offline fingerprint calibration is time consuming and labor intensive. In this study, inspired by our analysis that multi-density is inherent to the RSS distribution, we present a new radio map construction scheme, called Adaptive Density Graph-based Manifold Alignment (ADG-MA), which can reduce the number of Reference Points (RPs) in offline phase. In particular, it utilizes the density features to capture the exact neighborhood relations of RSS. Furthermore, the approach labels the RSS from user traces to construct the radio map. The extensive experiments demonstrate that the proposed method can construct an accurate radio map at a low deployment cost, as well as achieve a high localization accuracy.

Opportunistic-Target-Measurement-Based Narrowband Statistical Modeling of Civil Aviation Surveillance Signal at 1090 MHz

Automatic dependent surveillance—broadcast (ADS-B) is one of the next-generation aeronautical surveillance systems for air traffic control. ADS-B requires an aircraft to periodically broadcast its own position to other aircraft and ground stations, thereby enabling high-performance surveillance. In this study, the received signal strength (RSS) of the ADS-B signal was measured and characterized for opportunistic flights. The RSS distribution for a single aircraft was modeled as a sum of three components: nominal RSS, bias in the equivalent isotropically radiated power (EIRP), and fading. Then, the RSS distribution for multiple aircraft was defined, considering that the EIRP bias and fading parameter are different for different aircraft. To this end, a composite distribution was employed, the parameters of which were estimated from the bias and fading statistics. Furthermore, a practical approximation of the model was proposed. The proposed model is suitable for large-scale data, enabling the realization of aircraft-by-aircraft analysis. Also, it can provide a clear explanation of the mechanism by which the RSS distribution is formed.

A Novel System for WiFi Radio Map Automatic Adaptation and Indoor Positioning

WiFi fingerprinting-based indoor positioning system (IPS) using received signal strength (RSS) has been considered to be one solution for indoor positioning. However, there are two major bottlenecks that hamper its large-scale implementation. One widely recognized problem is the construction of a proper fingerprint database with high efficiency and accuracy. Second is to improve the online positioning accuracy on the basis of the fingerprint database. To address these issues comprehensively, this paper proposes a novel system-Digital navigation center IPS (DncIPS), an IPS that enables automatic online radio map construction, and step-by-step positioning, aiming for the high-accuracy RSS estimation and high-precision positioning. DncIPS can capture WiFi data packets transmitted in WiFi traffic so that they obtain the MAC addresses, frequency, and RSS of any WiFi access point (AP) at any point. DncIPS uses Gaussian process regression model based on a fireworks algorithm to approximate the RSS distribution of an indoor environment and to estimate the location of APs increasing the flexibility of DncIPS work environment. This system also consists of a coarse localizer detecting the outliers and dividing clustering area and a fine localizer followed to improve the online positioning accuracy. Extensive experiments results indicate the proposed system DncIPS leads to improvement on radio map updating and localization accuracy.

FUSION OF LOCATION FINGERPRINTING AND TRILATERATION BASED ON THE EXAMPLE OF DIFFERENTIAL WI-FI POSITIONING

Abstract. Positioning of mobile users in indoor environments with Wireless Fidelity (Wi-Fi) has become very popular whereby location fingerprinting and trilateration are the most commonly employed methods. In both the received signal strength (RSS) of the surrounding access points (APs) are scanned and used to estimate the user’s position. Within the scope of this study the advantageous qualities of both methods are identified and selected to benefit their combination. By a fusion of these technologies a higher performance for Wi-Fi positioning is achievable. For that purpose, a novel approach based on the well-known Differential GPS (DGPS) principle of operation is developed and applied. This approach for user localization and tracking is termed Differential Wi-Fi (DWi-Fi) by analogy with DGPS. From reference stations deployed in the area of interest differential measurement corrections are derived and applied at the mobile user side. Hence, range or coordinate corrections can be estimated from a network of reference station observations as it is done in common CORS GNSS networks. A low-cost realization with Raspberry Pi units is employed for these reference stations. These units serve at the same time as APs broadcasting Wi-Fi signals as well as reference stations scanning the receivable Wi-Fi signals of the surrounding APs. As the RSS measurements are carried out continuously at the reference stations dynamically changing maps of RSS distributions, so-called radio maps, are derived. Similar as in location fingerprinting this radio maps represent the RSS fingerprints at certain locations. From the areal modelling of the correction parameters in combination with the dynamically updated radio maps the location of the user can be estimated in real-time. The novel approach is presented and its performance demonstrated in this paper.

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting.

A method of location fingerprinting based on the Wi-Fi received signal strength (RSS) in an indoor environment is presented. The method aims to overcome the RSS instability due to varying channel disturbances in time by introducing the concept of invariant RSS statistics. The invariant RSS statistics represent here the RSS distributions collected at individual calibration locations under minimal random spatiotemporal disturbances in time. The invariant RSS statistics thus collected serve as the reference pattern classes for fingerprinting. Fingerprinting is carried out at an unknown location by identifying the reference pattern class that maximally supports the spontaneous RSS sensed from individual Wi-Fi sources. A design guideline is also presented as a rule of thumb for estimating the number of Wi-Fi signal sources required to be available for any given number of calibration locations under a certain level of random spatiotemporal disturbances. Experimental results show that the proposed method not only provides 17% higher success rate than conventional ones but also removes the need for recalibration. Furthermore, the resolution is shown finer by 40% with the execution time more than an order of magnitude faster than the conventional methods. These results are also backed up by theoretical analysis.

Investigating signal propagation and strength distribution characteristics of wireless sensor networks in date palm orchards

To efficiently plan and deploy wireless sensor networks in palm orchards, it is crucial to obtain preliminary knowledge of radio signal propagation and strength distribution characteristics. Various received signal strength indicator (RSSI) measurements were taken with antennas, operating with a 2.4-GHz band and located 0.05m away from trunks. The RSSI measurements, at seven different antenna heights along one certain horizontal line, revealed that there were three distinct signal propagation characteristics resulting from the morphological features of the date palm trees. Compared with other heights, the attenuation rate was lower when the antennas were placed below the crown base due to the relative lack of obstacles in the propagation paths, which also provided good options for installing the antennas. In addition, off-shoots had a slightly negative impact on signal propagation. Comparison analyses showed that the logarithm model was the most accurate and convenient of all the empirical signal attenuation models for predicting low power wireless signal propagation characteristics in date palm orchards. Further measurements were conducted to investigate the received signal strength distribution around individual trunks and between the orchards of different ages. Results indicated that the reliable communication range showed an increasing trend with increasing orchard age. Moreover, the trunks negatively impacted the signal propagation, and the interference strength depended on the position relationship among the trunks, the Tx and Rx antennas. Finally, the specific and detailed references were provided for the planning and deployment of wireless sensor networks in date palm orchards. Concluding remarks address potential future research.

RSS Distribution-Based Passive Localization and Its Application in Sensor Networks

Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application—the estimated trajectory is close to the ground truth.

Outdoor Location Estimation Using Received Signal Strength-Based Fingerprinting

A cluster-based intersection fingerprinting technique for outdoor location estimation using received signal strength (RSS) is proposed. The performance of the proposed scheme is demonstrated by making comparisons using RSS data from a simulated grid-based urban propagation model, RSS data generated by a network planning tool applied to a rural environment, and RSS data from real city environment. The proposed scheme first uses an optimal clustering scheme to portion the large outside area into different small regions based on the use of RSS deviations from the path loss model. For each region, a fine RSS distribution model is built to provide a good support for further positioning. An improved intersection method is then presented to find the most likely geographical area to further estimate a mobile user's location. A comparison between cluster-based and grid-based environment partitioning is made. The experimental results show that the proposed clustering scheme gives good support for location estimation and the positioning accuracy is significantly improved.

Estimation of Received Signal Strength Distribution for Smart Meters with Biased Measurement Data Set

This letter presents an experimental study and a novel modelling approach of the wireless channel of smart utility meters placed in basements or sculleries. The experimental data consist of signal strength measurements of consumption report packets. Since such packets are only registered if they can be decoded by the receiver, the part of the signal strength distribution that falls below the receiver sensitivity threshold is not observable. We combine a Rician fading model with a bias function that captures the cut-off in the observed signal strength measurements. Two sets of experimental data are analysed. It is shown that the proposed method offers an approximation of the distribution of the signal strength measurements that is better than a na\"ive Rician fitting.

Indoor positioning via subarea fingerprinting and surface fitting with received signal strength

The fingerprinting technique based on received signal strength (RSS) has been intensively researched for indoor localization in the last decade. Instead of using discrete reference points to build fingerprint database, this paper applies the surface fitting technique to construct RSS spatial distribution functions and proposes two location search methods to find the target location. We also propose to use subarea division and determination scheme to improve the fitting accuracy and search efficiency. In the offline phase, we divide the whole indoor environment into several subareas, construct a fingerprint for each subarea, and build a RSS distribution fitting function for each access point in each subarea. In the online phase, we first determine to which subarea a target belongs, and then search its location according to the proposed exhaustive location search or gradient descent based search algorithm. We conduct both extensive simulations and field experiments to verify the proposed scheme. The experiment results show that for the same reference point granularity, the proposed localization scheme can achieve about 22% localization accuracy improvement, compared with the classical nearest neighbor-based fingerprinting method.

Probabilistic Radio-Frequency Fingerprinting and Localization on the Run

Indoor localization is a key enabler for pervasive computing and network optimization. Wireless local area network (WLAN) positioning systems typically rely on fingerprints of received signal strength (RSS) measures from access points. In this paper, we review approaches for modeling full distributions of Wi-Fi signals, including Bayesian graphical models, smoothing, compressive sensing, and random field differentiation and concentrate on the Kullback-Leibler divergence metric that compares multivariate RSS distributions. We provide theoretical insights on the required spatial density of fingerprints and on the number of samples necessary, during tracking or during signal map building, to differentiate among signal distributions and to provide accurate location estimates. We validate our methods on contrasting datasets where we obtain state-of-the-art localization results. Finally, we exploit datasets collected by a self-localizing mobile robot that continuously records Wi-Fi along with ground truth position, where we define increasingly denser fingerprint grids and study asymptotic localization accuracy. ® 2014 Alcatel-Lucent.

Virtual 2D Positioning System by Using Wireless Sensors in Indoor Environment

A 2D location detection system is constructed by using Wireless Sensor Nodes (WSN) to create aVirtual Fingerprint map, specifically designed for use in an indoor environment. WSN technologies and programmable ZigBee wireless network protocols are employed. This system is based on radio-location fingerprinting technique. Both Linear taper functions and exponential taper functions are utilized with the received signal strength distributions between the fingerprint nodes to generate virtual fingerprint maps. Thus, areal and virtual combined fingerprint map is generated across the test area. K-nearest neighborhood algorithm has been implemented on virtual fingerprint maps, in conjunction with weight functions used to find the coordinates of the unknown objects. The system Localization accuracies of less than a grid space areproved in calculations.

An Improved Algorithm to Generate a Wi-Fi Fingerprint Database for Indoor Positioning

The major problem of Wi-Fi fingerprint-based positioning technology is the signal strength fingerprint database creation and maintenance. The significant temporal variation of received signal strength (RSS) is the main factor responsible for the positioning error. A probabilistic approach can be used, but the RSS distribution is required. The Gaussian distribution or an empirically-derived distribution (histogram) is typically used. However, these distributions are either not always correct or require a large amount of data for each reference point. Double peaks of the RSS distribution have been observed in experiments at some reference points. In this paper a new algorithm based on an improved double-peak Gaussian distribution is proposed. Kurtosis testing is used to decide if this new distribution, or the normal Gaussian distribution, should be applied. Test results show that the proposed algorithm can significantly improve the positioning accuracy, as well as reduce the workload of the off-line data training phase.

Validation of a Probabilistic Approach to Outdoor Localization

The validation of a probabilistic fingerprinting approach for outdoor location estimation using received signal strength (RSS) from GSM base stations (BSs) is described. The proposed approach is compared with a traditional probabilistic algorithm for three different area partitioning methods. Two contrasting real environments are used for the comparisons: one is a city environment and the other one is a rural setting. For each test-bed, over 9000 data points are collected over 170,000 and 110,000 square meters respectively. For each environment, principal components analysis (PCA) is globally used to remove the least useful transmitters to avoid unnecessary calculations. Then each environment is partitioned into different clusters based on RSS. PCA is again used within each cluster. The proposed scheme retains accuracy by not losing the substantial RSS correlations in each cluster, but also accommodates the different RSS distributions in each cluster. The experimental results show that the positioning accuracy is significantly improved and our clustering scheme gives good support for location estimation.

Kernel-based particle filtering for indoor tracking in WLANs

Indoor localization using signal strength in wireless local area networks (WLANs) is becoming increasingly prevalent in today’s pervasive computing applications. In this paper, we propose an indoor tracking algorithm under the Bayesian filtering and machine learning framework. The main idea is to apply a graph-based particle filter to track a person’s location on an indoor floor map, and to utilize the machine learning method to approximate the likelihood of an observation at various locations based on the calibration data. Nadaraya–Watson kernel regression is adopted to interpolate the Received Signal Strength (RSS) distribution for nonsurvey points. The success of the proposed kernel-based particle filter (KBPF) lies in the fact that KBPF incorporates the environmental and motion constraints into the model and restricts particles to propagate on the graph which precludes the locations that the person is unlikely to be at, and that the developed nonlinear interpolation method is effective in inferring the RSS distribution for the non-survey location points which makes it possible to reduce the total number of survey locations. In addition, missing value problem is addressed in this paper, and different methods are compared through experiments. We conducted a series of experiments in a typical office environment. Results show that KBPF achieves superior performance than other existing algorithms. It even yields higher accuracy with only a small fraction of training data than others with a full training data set. As a consequence, by applying KBPF, sub-meter accuracy can be obtained while extensive calibration effort can be greatly reduced. Although KBPF is more computationally complex, it can still provide real time estimates.

Nonparametric Model Comparison and Uncertainty Evaluation for Signal Strength Indoor Location

Indoor location (IL) using received signal strength (RSS) is receiving much attention, mainly due to its ease of use in deployed IEEE 802.11b (Wi-Fi) wireless networks. Fingerprinting is the most widely used technique. It consists of estimating position by comparison of a set of RSS measurements, made by the mobile device, with a database of RSS measurements whose locations are known. However, the most convenient data structure to be used and the actual performance of the proposed fingerprinting algorithms are still controversial. In addition, the statistical distribution of indoor RSS is not easy to characterize. Therefore, we propose here the use of nonparametric statistical procedures for diagnosis of the fingerprinting model, specifically: 1) A nonparametric statistical test, based on paired bootstrap resampling, for comparison of different fingerprinting models and 2) new accuracy measurements (the uncertainty area and its bias) which take into account the complex nature of the fingerprinting output. The bootstrap comparison test and the accuracy measurements are used for RSS-IL in our Wi-Fi network, showing relevant information relating to the different fingerprinting schemes that can be used.