In wireless networks, radio-map based locating techniques are commonly used to cope the complex fading feature of radio signal, in which a radio-map is built by calibrating received signal strength (RSS) signatures at training locations in the offline phase. However, in severe hostile environments, such as in ship cabins where severe shadowing, blocking and multi-path fading effects are posed by ubiquitous met allic architecture, even radio-map cannot capture the dynamics of RSS. In this paper, we introduced multiple feature radio-map location method for severely noisy environments. We proposed to add low variance signature into radio map. Since the low variance signatures are generally expensive to obtain, we focus on the scenario when the low variance signatures are sparse. We studied efficient construction of multi-feature radio-map in offline phase, and proposed feasible region narrowing down and particle based algorithm for online tracking. Simulation results show the remarkably performance improvement in terms of positioning accuracy and robustness against RSS noises than the traditional radio-map method.
To improve the positioning accuracy, based approach was proposed to model the diverse fading signatures of radio signal @cite_16 @cite_9 @cite_2 . This method contains an offline and an online phase. In offline phase, @math training locations are selected in the sensing field, which are denoted by @math . Suppose there are @math beacons (WiFi APs or wireless sensors) in the sensing field, which are denoted by @math . In the training phase, the RSS values of all beacons at each training location @math will be measured over a period of time, so that a vector of location @math is constructed as @math . When only mean value of RSS is considered, @math represents the average RSS value from @math . When signature distribution is considered, @math can be probabilistic density function (pdf) of RSS from @math . The signature vectors of all training locations are stored as a database, called , denoted by @math .
@cite_9 @cite_16 @cite_2
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The recent growing interest for indoor Location-Based Services (LBSs) has created a need for more accurate and real-time indoor positioning solutions. The sparse nature of location finding makes the theory of Compressive Sensing (CS) desirable for accurate indoor positioning using Received Signal Strength (RSS) from Wireless Local Area Network (WLAN) Access Points (APs). We propose an accurate RSS-based indoor positioning system using the theory of compressive sensing, which is a method to recover sparse signals from a small number of noisy measurements by solving an 1-minimization problem. Our location estimator consists of a coarse localizer, where the RSS is compared to a number of clusters to detect in which cluster the node is located, followed by a fine localization step, using the theory of compressive sensing, to further refine the location estimation. We have investigated different coarse localization schemes and AP selection approaches to increase the accuracy. We also show that the CS theory can be used to reconstruct the RSS radio map from measurements at only a small number of fingerprints, reducing the number of measurements significantly. We have implemented the proposed system on a WiFi-integrated mobile device and have evaluated the performance. Experimental results indicate that the proposed system leads to substantial improvement on localization accuracy and complexity over the widely used traditional fingerprinting methods. With the technical advances in ubiquitous comput- ing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoreti- cal base and develop a localization algorithm for building a zero- configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our division building. The empirical results show the proposed system is quite robust and gives accurate localization results (i.e., with the localization error within 3 meters). In this paper, we are interested in counting the total number of people walking in an area based on only WiFi received signal strength indicator (RSSI) measurements between a pair of stationary transmitter receiver antennas. We propose a framework based on understanding two important ways that people leave their signature on the transmitted signal: blocking the line of sight (LOS) and scattering effects. By developing a simple motion model, we first mathematically characterize the impact of the crowd on blocking the LOS. We next probabilistically characterize the impact of the total number of people on the scattering effects and the resulting multipath fading component. By putting the two components together, we then develop a mathematical expression for the probability distribution of the received signal amplitude as a function of the total number of occupants, which will be the base for our estimation using Kullback-Leibler divergence. To confirm our framework, we run extensive indoor and outdoor experiments with up to and including nine people and show that the proposed framework can estimate the total number of people with a good accuracy with only a pair of WiFi cards and the corresponding RSSI measurements. Wireless Indoor localization systems based on RSSI-values typically consist of an offline training phase and online position determination phase. During the offline phase, geo-referenced RSSI measurements, called fingerprints, are recorded to build a radio-map of the building. This radiomap is then searched during the position determination phase to estimate another nodes' location. Usually the radiomap is build manually, either by users pin-pointing their location on a ready-made floorplan or by moving in pre-specified patterns while scanning the network for RSSI values. This cumbersome process leads to inaccuracies in the radiomap. Here, we propose a system to build the floorplan and radio-map simultaneously by employing a handheld laser mapping system in an IEEE802.15.4-compatible network. This makes indoor- and radio-mapping for wireless localization less cumbersome, faster, more reliable and delivers a new way to evaluate wireless localization systems.