Compressive Sensing Based Multiple Source Localization in the Presence of Sensor Position Uncertainty and Nonuniform Noise

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.

Source localization using RSS (received signal strength) measurements has received considerable attention recently. However, there are few works focusing on the problem of positioning multiple directional sources. To provide a benchmark for evaluating localization algorithms, this paper derives the Cramer-Rao lower bounds (CRLB) for estimating locations, orientations, transmit powers, beam widths of multiple directional sources and the path loss exponent (PLE) of the environment. Meanwhile, in order to facilitate the computation and expression, we also present the relationship of the CRLB among different quantities of sources when estimating their locations and orientations. Given that the maximum likelihood (ML) estimator for the localization problem is highly non-convex and non-linear, this paper proposes an Expectation Maximization (EM) like algorithm to estimate the multiple parameters of each of the sources iteratively. Moreover, the complexity of the presented ML-EM algorithm is significantly reduced via linear representation among the parameters to be estimated. Numerical and simulation results demonstrate the performance of the proposed methods and the derived bounds in terms of estimation error, noise robustness and number of sources.

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

Calibrating the error from sensor position uncertainty in TDOA-AOA localization

The algorithms based on the technique of optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding (OT) were recently proposed for signal recovery, and they are very different from the traditional family of hard thresholding methods. However, the computational cost for OT-based algorithms remains high at the current stage of their development. This stimulates the development of the so-called natural thresholding (NT) algorithm and its variants in this paper. The family of NT algorithms is developed through the first-order approximation of the so-called regularized optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding model, and thus the computational cost for this family of algorithms is significantly lower than that of the OT-based algorithms. The guaranteed performance of NT-type algorithms for signal recovery from noisy measurements is shown under the restricted isometry property and concavity of the objective function of regularized optimal <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-thresholding model. Empirical results indicate that the NT-type algorithms are robust and very comparable to several mainstream algorithms for sparse signal recovery.

Typical WLAN based indoor positioning systems take the received signal strength (RSS) as the major information source. Due to the complicated indoor environment, the RSS measurements are hard to model and too noisy to achieve a satisfactory 3-D accuracy in multi-floor scenarios. To enhance the performance of WLAN positioning systems, extra information sources could be integrated. In this paper, a Bayesian framework is applied to fuse multi-information sources and estimate the spatial and time varying parameters simultaneously and adaptively. An application of this framework, which fuses pressure measurements, a topological building map with RSS measurements, and simultaneously estimates the pressure sensor bias, is investigated. Our experiments indicate that the localization performance is more accurate and robust by using our approach.

Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this paper, we propose a new formulation that unifies received-signal-strength (RSS) and direction of arrival (DoA) in a compressive sensing (CS) framework. The underlying CS measurement matrix is a function of beamforming vectors of sensors and is referred to as the propagation matrix. Comparing to the omni-directional antenna case, our employed propagation matrix provides more incoherent projections, an essential factor in the compressive sensing theory. Based on the new formulation, we optimize the antenna beams, enhance spectrum sensing efficiency, track active primary users accurately and monitor spectrum activities in an area of interest. In many practical scenarios there is no fusion center to integrate received data from spectrum sensors. We propose the distributed version of our algorithm for such cases. Experimental results show a significant improvement in source localization accuracy, compared with the scenario when sensors are equipped with omni-directional antennas. Applicability of the proposed framework for dynamic radio cartography is shown. Moreover, comparing the estimated dynamic RF map over time with the ground truth demonstrates the effectiveness of our proposed method for accurate signal estimation and recovery.

This paper investigates the problem of received signal strength (RSS) based target localization with sensor position uncertainties and unknown model parameters. Transmit power and path-loss exponent (PLE) are two key parameters of the path-loss model that is critical for RSS schemes to work well. In practice, however, these parameters change dynamically and are difficult to estimate in real time. Four combinations of the two parameters are considered: both parameters are known; only one of the two is known; and both are unknown. We develop new semidefinite programming (SDP) methods to relax the nonconvex maximum likelihood estimator (MLE) problem for each case, and these methods are further optimized when sensor position uncertainties are taken into consideration. We present a comprehensive set of simulation results to compare the performance of the proposed scheme with existing methods.

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.

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.

The cloud radio access network (C-RAN) is a promising network architecture for future mobile communications, and one practical hurdle for its large scale implementation is the stringent requirement of high capacity and low latency fronthaul connecting the distributed remote radio heads (RRH) to the centralized baseband pools (BBUs) in the C-RAN. To improve the scalability of C-RAN networks, it is very important to take the fronthaul loading into consideration in the signal detection, and it is very desirable to reduce the fronthaul loading in C-RAN systems. In this paper, we consider uplink C-RAN systems and we propose a distributed fronthaul compression scheme at the distributed RRHs and a joint recovery algorithm at the BBUs by deploying the techniques of distributed compressive sensing (CS). Different from conventional distributed CS, the CS problem in C-RAN system needs to incorporate the underlying effect of multi-access fading for the end-to-end recovery of the transmitted signals from the users. We analyze the performance of the proposed end-to-end signal recovery algorithm and we show that the aggregate measurement matrix in C-RAN systems, which contains both the distributed fronthaul compression and multiaccess fading, can still satisfy the restricted isometry property with high probability. Based on these results, we derive tradeoff results between the uplink capacity and the fronthaul loading in C-RAN systems.

The fingerprint location method based on the WiFi received signal strength (RSS), is one of the indoor positioning method owning the highest value of application currently. It uses distribution characteristic of WiFi RSS in the space to estimate user's location. But due to the unstable RSS measurements of WiFi signal in a fixed location, in online phase, the uncertain fluctuation of RSS measurements will cause matching error of reference point (RP), and lead to the positioning error. To deal with this problem, a matching positioning method combining spatial multi-points fingerprint is presented in this paper. Firstly, assisted by odometer information, the space uniforming calibration of fingerprint is carried out in radial direction of road. And then, intensity and variation characteristics in space of consecutive multi-points RSS are matched jointly in online process, so as to enhance the positioning accuracy and stability. Through experiments, it is proved that the method in this paper is effective and, under reasonable parameters, positioning performance is good.

Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in most mobile devices. The RSS channel model is defined by the propagation losses and the shadow fading. These parameters might vary over time because of changes in the environment. In this paper, the problem of tracking a mobile node by RSS measurements is addressed, while simultaneously estimating a two-slope RSS model. The methodology considers a Kalman filter with Interacting Multiple Model architecture, coupled to an on-line estimation of the observation's variance. The performance of the method is shown through numerical simulations in realistic scenarios.

With the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications.

Received signal strength (RSS) based source localization method is popular due to its simplicity and low cost. However, this method is highly dependent on the propagation model which is not easy to be captured in practice. Moreover, most existing works only consider the single source and the identical measurement noise scenario, while in practice multiple co-channel sources may transmit simultaneously, and the measurement noise tends to be nonuniform. In this paper, we study the multiple co-channel sources localization (MSL) problem under unknown nonuniform noise, while jointly estimating the parametric propagation model. Specifically, we model the MSL problem as being parameterized by the unknown source locations and propagation parameters, and then reformulate it as a joint parametric sparsifying dictionary learning (PSDL) and sparse signal recovery (SSR) problem which is solved under the framework of sparse Bayesian learning with iterative parametric dictionary approximation. Furthermore, multiple snapshot measurements are utilized to improve the localization accuracy, and the Cramer-Rao lower bound (CRLB) is derived to analyze the theoretical estimation error bound. Comparing with the state-of-the-art sparsity-based MSL algorithms as well as CRLB, extensive simulations show the importance of jointly inferring the propagation parameters,and highlight the effectiveness and superiority of the proposed method.