Kaide Huang

An Exponential-Rayleigh Model for RSS-Based Device-Free Localization and Tracking

A common technical difficulty in device-free localization and tracking (DFLT) with a wireless sensor network is that the change of the received signal strength (RSS) of the link often becomes more unpredictable due to the multipath interferences. This challenge can lead to unsatisfactory or even unstable DFLT performance. This work focuses on developing a new RSS model, called Exponential-Rayleigh (ER) model, for addressing this challenge. Based on data from our extensive experiments, we first develop the ER model of the received signal strength. This model consists of two parts: the large-scale exponential attenuation part and the small-scale Rayleigh enhancement part. The new consideration on using the Rayleigh model is to depict the target-induced multipath components. We then explore the use of the ER model with a particle filter in the context of multi-target localization and tracking. Finally, we experimentally demonstrate that our ER model outperforms the existing models. The experimental results highlight the advantages of using the Rayleigh model in mitigating the multipath interferences thus improving the DFLT performance.

A real-time device-free localization system using correlated RSS measurements

Device-free localization (DFL) with wireless sensor networks (WSN) is an emerging technology for target localization, which has received much attention in the area of Internet of Things. Received signal strength (RSS) measurements are the key to realize DFL and mainly affects the localization performance. Most existing approaches need to measure the RSS of all the wireless links in WSN, which take much time on measurement process and localization algorithm due to the large amounts of RSS data, thus they are inefficient, especially in the case of target tracking. In this paper, by making full use of the consecutiveness of motion, we present an efficient measurement strategy based on a small set of correlated wireless links. Furthermore, a lightweight compressed maximum matching select (CMMS) algorithm is proposed to localize target, which only needs a small-scale matrix-vector product operating for one estimation. The proposed approach can significantly reduce the number of RSS measurements and improve the real-time capability of the DFL system. Experimental results demonstrate the superior performance of the proposed method in the context of target localization and tracking.

Heterogeneous Bayesian compressive sensing for sparse signal recovery

This study focuses on the issue of sparse signal recovery with sparse Bayesian learning in the context of a heterogeneous noise model, called by the heterogeneous Bayesian compressive sensing. The main contribution is to exploit the capability of noise variance learning in performance improvement and applicability enhancement. Experimental results on synthetic and real-world data demonstrate that heterogeneous Bayesian compressive sensing has superior performance in terms of accuracy and sparsity for both homogeneous and heterogeneous noise scenarios.

An Exponential-Rayleigh signal strength model for device-free localization and tracking with wireless networks

We present a new statistic signal strength model, called Exponential-Rayleigh (ER) model, for device-free (DF) target localization and tracking issues in this paper. It is a single target measurement model for radio frequency (RF) based on received signal strength (RSS) measurement in outdoor regions. The model is a non-linear function between RSS measurements and target motion state. It consists of three parts: the largescale exponential attenuation part, the small-scale Rayleigh enhancement part and the noise. Different from the proposed models, while reserving the large-scale attenuation, we mainly present the small-scale Rayleigh enhancement model in ER model. The Rayleigh part depicts the multi-path caused by single target so as to reduce the multi-path error. In the context of localization and tracking experiment using particle filter, we validate the effectiveness of ER model.

A hierarchical RSS model for RF-based device-free localization

One common challenge in device-free localization (DFL) with radio frequency sensor networks is that the variations of received signal strength (RSS) become less sensitive to shadow fading and the performance will be degenerated in a cluttered environment. To address this problem, this paper develops a hierarchical model that contributes to refining the description granularity of RSS variations and enhancing the sensitivity of RSS variations to shadow fading. The novelty is twofold. First, the Exponential-Rayleigh model and the diffraction-based model are integrated to form a two degree-of-freedom parametrization that can capture not only the target-induced multipath interferences but also the diffraction fading contributions. Second, the link dependent characterizations of RSS variations are explored by parameterizing the model coefficients in terms of fade level. The proposed model is experimentally validated in the context of radio tomographic imaging (RTI).

A Diffraction Based Modified Exponential Model for Device-Free Localization with RSS Measurements

Radio frequency (RF) based Device-free localization and tracking (DFLT) monitors the change in received signal strength (RSS) measurements to locate the targets without carrying any electronic devices in the sensing area covered by a RF sensor network. This paper presents a new modified exponential model to accurately describe the relationship between the RSS measurements and the target state, which can effectively predict the variation of RSS when the target is present on line-of-sight (LOS) path or non-line-of-sight (NLOS) path. Based on the diffraction theory, we first show that the RSS attenuation caused by target mainly depends on two factors: the target-nodes distance and the target-link distance, which can be exploited to depict the change of RSS on LOS and NLOS, respectively. By taking into account these two factors, we then develop our model, and validate it with single link experiments. We finally explore the use of the proposed model with particle filter for DFLT, and demonstrate that our model can improve the DFLT performance by conducting actual experiments.

Enhanced radio tomographic imaging with heterogeneous Bayesian compressive sensing

Abstract This work explores the novel use of Bayesian compressive sensing (BCS) in radio tomographic imaging (RTI), which aims at addressing the performance degradation of shadow fade imaging due to multipath interferences, through the sophisticated efforts on enhancing BCS with the capability of heterogeneous-noise-variance learning. Our contribution is twofold. Firstly, we incorporate a hierarchical model of heterogeneous noise variances into sparse Bayesian learning, which can contribute to the enhancement of BCS in terms of noise-variance awareness. Then, under our enhanced BCS (namely heterogeneous BCS) framework, we develop two learning algorithms for the RTI reconstruction. Theoretical analysis will show the potential advantages of using our heterogeneous BCS in mitigating the effect of multipath interferences, as well as in improving the RTI performance with our learning algorithms. Finally, the experimental results in the context of device-free localization and tracking are reported to demonstrate the effectiveness of the proposed approach.

An enhanced multi-scale model for shadow fading in radio tomographic imaging

This paper focuses on the spatial inhomogeneity of shadow fading in radio tomographic imaging. An enhanced multi-scale model is developed to characterize the spatial inhomogeneity of shadow fading, which aims at offering an insight into the correlation between the spatial scales and the fading levels, thus enhances the capability of capturing the shadow fading components. It should be highlighted that, when the RSS value of a link increases significantly, the proposed model can predict the impossibility of LOS targets with high probability, which can benefit the accuracy improvement of localization. This is our new contribution to the existing multi-scale shadow fading models. The experimental results are reported to validate the proposed model in the context of target localization with radio tomographic imaging.

Sparse Bayesian Learning with joint noise robustness and signal sparsity

This study concerns the issue of jointly enhancing noise robustness and promoting signal sparsity in Sparse Bayesian Learning (SBL), which aims at addressing the performance deficiency of sparse signal recovery due to uninformative data with low signal-to-noise ratios. In particular, the authors propose a hierarchical prior noise model with a signal-dependent parametrisation and incorporate it into developing the robust SBL algorithms for sparse signal recovery. The main contribution of the proposed approach is twofold. The first is the new consideration of noise-robustness enhancement in building SBL algorithms, which devotes to noise awareness in counteracting outliers in measurements. Specifically, the idea of signal-sparsity enforcing is extended to build a Least Absolute Deviation like loss criterion with the proposed hierarchical prior model of measurement noise. The second is the novelty of using the signal-dependent parametrisation in the proposed noise model. Indeed, the signal-dependent mechanism plays an indispensable role in producing the reliable noise parameter estimation jointly with updating signal model parameters under the fast SBL framework. In addition to numerical simulation studies, the real-life application of radio tomographic imaging is presented to validate the proposed approach.