UWB body motion assisted indoor geolocation with a single reference point

The paper focuses on the performance analysis of the timing acquisition scheme based on received signal strength (RSS) measurements for ultra-wideband (UWB) communication systems. To our knowledge, few paper discusses the performance of the timing acquisition scheme based on RSS measurements for UWB in details. The timing acquisition scheme based on RSS measurements has many advantages, such as low cost, high speed and robustness. However, low transmission power limitation and complicated indoor multipath propagation environments make it difficult to measure accurate RSS at the receiver and affect the accuracy of the time delay offset estimation for the local template signals. In addition, the time variation of the indoor channel state can form a multiple-to-one mapping relation between the RSS measurements and the distance, which largely limits the method application. In the paper, we analyze the accuracy of time delay offset estimation for this method by deriving the Cramer-Rao lower bound (CRLB). The number results show that the accuracy of the timing acquisition method based on RSS measurements suffers from both the UWB signal transmission speed and the varying distance between the transmitter and receiver. It is noted that though the accuracy of the method can hardly fulfil the fine timing acquisition requirement for UWB systems, it will largely reduce the coarse timing acquisition time and improve the performance for UWB timing acquisition systems if combined with other timing acquisition ones

For wireless sensor networks, received signal strength (RSS) and proximity (also known as connectivity) measurements have been proposed as simple and inexpensive means to estimate range between devices and sensor location. While RSS measurements are recognized to suffer from errors due to the random nature of the fading channel, proximity measurements, ie., knowing only whether or not two devices are in communication range, are often discussed without considering that they are affected by the same fading channel. Proximity measurements are actually just a binary quantization of RSS measurements. We use the Cramér-Rao bound (CRB) to compare the minimal attainable variances of unbiased sensor location estimators for the cases of RSS and proximity measurements. For completeness, we also present the CRB for sensor localization with systems using K-level quantized RSS (QRSS) measurements, of which proximity measurements are the special case: K=2. Examples are presented for the case of one unknown-location sensor, and for the case of a 5 by 5 grid of sensors. These examples show that lower bounds for standard deviation in proximity-based systems are, as a rule of thumb, about 50% higher than the bounds for RSS-based systems. Furthermore, results are presented which show how many bits of quantization are necessary for a QRSS-based system to nearly achieve the bounds of an unquantized RSS system.

There is much current interest in wireless networking of sensors deployed on and around the human body, in view of many potential applications for health monitoring. In this paper, we conducted ultra-wide-band (UWB) measurements around human body and a phantom in order to develop angle based channel models for body area network (BAN) and to compare the measurement results in these two environments. By analyzing certain channel parameters such as, time-of-arrival (TOA), distance-measurement-error (DME), received signal strength (RSS) and total path-loss, we discuss the influencing factors when the signal traveling around the human body. We also analyze the influence of the human body on TOA ranging accuracy.

Due to the significance of positioning in complicated electromagnetic environment of its variety of applications, wireless localization techniques have attracted significant research interests with the trends moving toward boosting the capabilities of multi-objective cooperative networks. This paper addresses the problem of locating multiple targets in 3-D cooperative wireless sensor network. A novel hybrid received signal strength (RSS) and time-of-arrival (TOA) positioning scheme is proposed. Employing RSS and TOA measurements, four valuable expressions are drawn. The TOA and RSS technologies are combined with path loss difference variable being introduced. Based on the weighted least squares criterion, the rough solutions of multiple targets positions are derived. Finally, the locations of obtained targets are optimized by cooperative wireless communication between the target and another target. The location accuracy of the proposed estimation algorithm is analyzed and compared with the conventional RSS and TOA estimation algorithms through simulation experiments. The simulation result demonstrates that the proposed hybrid RSS and TOA localization algorithm for multi-objective cooperative wireless networks outperforms traditional approaches.

A malicious attacker could, by taking control of internet-of-things devices, use them to capture received signal strength (RSS) measurements and perform surveillance on a person's vital signs, activities, and sound in their environment. This article considers an attacker who looks for subtle changes in the RSS in order to eavesdrop sound vibrations. The challenge to the adversary is that sound vibrations cause very low amplitude changes in RSS, and RSS is typically quantized with a significantly larger step size. This article contributes a lower bound on an attacker's monitoring performance as a function of the RSS step size and sampling frequency so that a designer can understand their relationship. Our bound considers the little-known and counter-intuitive fact that an adversary can improve their sinusoidal parameter estimates by making some devices transmit to add interference power into the RSS measurements. We demonstrate this capability experimentally. As we show, for typical transceivers, the RSS surveillance attacker can monitor sound vibrations with remarkable accuracy. New mitigation strategies will be required to prevent RSS surveillance attacks.

Non-line-of-sight (NLOS) propagation can severely degrade the reliability of communication and localisation accuracy in indoor ultra-wideband (UWB) ‘location-aware’ networks. Link adaptation and NLOS bias mitigation techniques have respectively been proposed to alleviate these effects, but implicitly rely on the ability to accurately distinguish between LOS and NLOS propagation scenarios. A statistical NLOS identification technique based on the hypothesis-testing of received signal parameters in UWB propagation channels is discussed. In contrast to narrowband and wideband signals, UWB signals possess higher temporal resolution and robustness to multipath fading. We show that these characteristics result in differences in the statistics of (a) the time-of-arrival (TOA), (b) the received signal strength (RSS) and (c) the root-mean-squared delay spread (RDS) of the received signals, between LOS and NLOS propagation scenarios, which can be exploited for accurate channel identification. We statistically characterise the ability of TOA, RSS and RDS estimates to distinguish between LOS and NLOS propagation based on an extensive indoor measurement campaign. Our measurement results suggest that the RDS of UWB signals can, even in isolation and without complete statistical information, serve as a robust and computationally efficient indicator of the LOS/NLOS nature of propagation. Finally, we demonstrate the efficacy of the discussed NLOS identification method in a location-tracking application based on indoor UWB measurements.

As an emerging technique with a promising application prospect, the device-free localization (DFL) technique has drawn considerable attention due to its ability of realizing wireless localization without the need of equipping the target with any device. The DFL technique detects the shadowed links and realizes localization with the received signal strength (RSS) measurements of these links. However, one major disadvantage of the DFL technique is that the RSS signal is too sensitive, and a slight variation of the environment will cause the variation of RSS measurements, which incurs the misjudgment of shadowed links and degradation of localization performance. To solve this problem, a robust DFL scheme based on differential RSS is proposed. The scheme utilizes the novel differential RSS to judge whether a link is shadowed, which not only eliminates the need of acquiring reference RSS measurements but also overcomes the negative effect incurred by the environment. Meanwhile, an outlier detection scheme is presented to filter out the outlier links that are far away from the target. We present the observation model of the shadowed links and incorporate it into the particle filter framework to realize location estimation robustly. Experimental results demonstrate the outstanding performance of the proposed scheme.

In this paper, we consider the performance of received signal strength (RSS) and time of arrival (TOA) under different areas of primary user (PU) position estimation. The areas of PU position can be separated into two cases: inner area and outer area of boundary of secondary user (SU) nodes. This boundary is created by convex hull algorithm. In simulation result, we found that RSS is appropriated to use in inner area and TOA is suitable to use in outer area. Therefore, in this paper, we propose two hybrid location estimation techniques that combine RSS and TOA in cognitive radio (CR) system. The proposed techniques include the hybrid TOA/RSS in serial and the hybrid TOA/RSS in parallel technique. Simulation results show that the proposed techniques have better performance than the methods that use only RSS or TOA. Moreover, the proposed techniques are faster than TOA about 60%.

In this work we investigate position-based enhancements to indoor Wi-Fi network security. Specifically, we investigate whether received signal strength (RSS) measurements can identify attempts at network access by malicious nodes exterior to an authorized network perimeter. We assume the malicious nodes will spoof their received or transmitted power-levels in attempts to circumvent standard position-based security techniques. We outline why residual analysis of the RSS measurements cannot robustly identify illegal network access requests. However, we show that by referencing the residual RSS analysis to a "claimed position," interior to the authorized perimeter, a robust position-based verification system for secure network access can be developed.

This paper investigates the problem of estimating the location and velocity of a mobile agent using the received signal strength (RSS) measurements. Typical power measurements are inherently nonlinear and in this approach we derive a linear measurement scheme using an analytical measurement conversion technique which can readily be used with RSS measuring sensors. Power measurements are hence used in our robust version of a linear Kalman filter to estimate the dynamic parameters of the moving transmitter.

Location estimation within the human body by means of wireless signals is becoming popular for a variety of purposes, including wireless endoscopy using camera pills. The precision of wireless ranging in any medium is contingent upon the methodology employed. Two of the most popular wireless tracking methods are received signal strength (RSS) and time of arrival (TOA). The scope of this study is an assessment of the precision of TOA- and RSS-based ranging in the proximity of anthropomorphic tissue by means of simulation software designed to mimic signal transmission in the human body environment. Software simulations of wireless signals traveling within a human body are exceptionally challenging and require extensive computational resources. We created a rudimentary, MATLAB script using the finite-difference time-domain (FDTD) method to simulate the signal transmission inside and outside a human body and correlated the simulation outcomes of this script with the high-end commercial finite-element method (FEM) tool, ANSYS HFSS. First, we demonstrated that the FDTD modeling produces similar outcomes. Next, we employed the script to emulate the RSS and TOA of the wide bandwidth radio transmission within the human body for wireless ranging and estimated the accuracy of each technology. The precision of both methods was also evaluated with the Cramer-Rao lower bound (CRLB), which is frequently used to estimate the ranging methodologies and the effect of human tissue and its motion.

In this paper, a new framework is proposed for least-squares localization based on estimated ranges, covering time-difference-of-arrival (TDoA), time-of-arrival (ToA), and received signal strength (RSS) cases. The multidimensional nonlinear localization problem is first transformed to a lower dimension and then solved iteratively. Within the proposed transformed least-squares (TLS) framework, we introduce a method in which the localization problem is transformed to one dimension (1-D). In this way, compared to the classical nonlinear least-squares (NLS) type of methods, the amount of computations in each iteration is greatly reduced; a reduction of 67% for a 3-D positioning system is shown. Hence, the introduced 1-D iterative (1DI) method is fairly light on the computational load. The way to choose the 1-D parameter is proposed, and theoretical expressions for the convergence rate and the root- mean-squared error (RMSE) of the 1DI estimator are derived. Validation is performed mainly based on actual ultra-wideband (UWB) radio measurements, collected in typical office environments, with signal bandwidths varying from 0.5 to 7.5 GHz. Supplementary simulations are also included for validation. Results show that, in terms of RMSE, the 1DI method performs better than the linear least-squares (LLS) method, where the solution is obtained noniteratively, and performs similarly as NLS, especially in TDoA cases.

The location of mobile terminals in cellular networks is an important problem with applications in resource allocation, location sensitive browsing, and emergency communications. Finding cost effective location estimation techniques that are robust to non-line of sight (NLOS) propagation, quantization, and measurement noise is a key problem in this area. Quantized time difference of arrival (TDoA) and received signal strength (RSS) measurements can be made simultaneously by CDMA cellular networks at low cost. The different sources of errors for each measurement type cause RSS and TDoA measurements to contain independent information about mobile terminal location. This paper applies data fusion to combine the information of RSS and TDoA measurements to calculate a superior location estimate. Nonparametric estimation methods, that are robust to variations of measurement noise and quantization, are employed to calculate the location estimates. It is shown how the data fusion location estimators are robust, provide lower error than the estimators based on the individual measurements, and have low implementation cost.

Wireless intruder geolocation techniques using received signal strength (RSS) measurements from an array of sensors are very attractive for campus wireless network security because of their low cost implementation and simplicity. Both precision in RSS measurements and the relative geometry between RF sensors and the wireless intruder can affect the geolocation accuracy. This paper presents a comparative performance analysis of three RSS-based geolocation algorithms for non-cooperative wireless intruders in terms of their root mean square errors in wireless campus networks. A comparison study is established in a RF sensor grid and circular sensor geometries to fit general campus topology. Geolocation algorithm accuracy is examined for these particular geometric configurations of RF sensors with respect to various wireless intruder locations in and around a campus area. The results show that certain geolocation algorithms perform much better than others when wireless intruders are inside a campus wireless network, whereas the performance of the same geolocation algorithms is turned around when wireless intruders are outside a campus wireless network. As a result, a high accuracy solution to wireless intruder geolocation in campus wireless networks is discovered and proposed. MATLAB-based numerical simulations were used to evaluate the performance of these algorithms for various scenarios and parameters.

Indoor localization is often challenging due to the non-availability of GPS signals. Recently, various radio frequency fingerprinting techniques have been proposed to identify indoor locations using simply received signal strength (RSS) measurements. In general however, RSS measurements are time-varying and are difficult to model for complex environments. This paper proposes the use of dictionary learning (DL) to generate high quality fingerprints that depend also on the channel characteristics for each location. An enhanced DL algorithm is proposed that utilizes prior information about the channel distribution, and can generate the fingerprints in an online fashion. Simulation results demonstrate the efficacy of the proposed approach.