A Diffraction Measurement Model and Particle Filter Tracking Method for RSS-Based DFL

This paper studies radio propagation mechanisms that impact handoffs, air interface design, beam steering, and MIMO for 5G mobile communication systems. Knife edge diffraction (KED) and a creeping wave linear model are shown to predict diffraction loss around typical building objects from 10 to 26 GHz, and human blockage measurements at 73 GHz are shown to fit a double knifeedge diffraction (DKED) model which incorporates antenna gains. Small-scale spatial fading of millimeter wave received signal voltage amplitude is generally Ricean-distributed for both omnidirectional and directional receive antenna patterns under both line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in most cases, although the log-normal distribution fits measured data better for the omnidirectional receive antenna pattern in the NLOS environment. Small-scale spatial autocorrelations of received voltage amplitudes are shown to fit sinusoidal exponential and exponential functions for LOS and NLOS environments, respectively, with small decorrelation distances of 0.27 cm to 13.6 cm (smaller than the size of a handset) that are favorable for spatial multiplexing. Local area measurements using cluster and route scenarios show how the received signal changes as the mobile moves and transitions from LOS to NLOS locations, with reasonably stationary signal levels within clusters. Wideband mmWave power levels are shown to fade from 0.4 dB/ms to 40 dB/s, depending on travel speed and surroundings.

show abstract

“…Diffraction loss is calculated via numerical approximation by Fresnel integration and the diffraction parameter as follows [52]:…”

Section: Ked Blockage Modelmentioning

confidence: 99%

“…The complex signals corresponding to the edges can be added in order to determine the combined diffraction loss observed at the RX. The total diffraction loss power in log-scale is determined by taking the magnitude squared of the summed signals as follows [52]:…”

Section: Ked Blockage Modelmentioning

confidence: 99%

Small-Scale, Local Area, and Transitional Millimeter Wave Propagation for 5G Communications

Rappaport

MacCartney

Sun

et al. 2017

IEEE Trans. Antennas Propagat.

144

121

View full text Add to dashboard Cite

show abstract

“…Bayesian DFL methods, however, directly track the target, which is accomplished by first modeling RSS measurements as the function of the target’s state and subsequently using Bayesian filter to estimate the state of the target. The current measurement models in Bayesian methods include elliptical model [14], exponential model [8,12,18], diffraction model [19,20] and three-state model [21], with complexity sorted from lowest to highest. Since the models are all nonlinear with respect to the position of the target, nonlinear filtering such as particle filtering (PF) can be applied to track the target.…”

Section: Related Workmentioning

confidence: 99%

“…The classical Kalman filter, which is a type of Bayesian filter for the linear/Gaussian case, is not suitable anymore due to the nonlinearity of the link state model. Fortunately, in recent years particle filter (PF) has been widely employed to deal with nonlinear filtering [8,9,12,16,17,18,19,20,21]. In this paper, we perform target tracking in the binary work mode by employing PF which proves to be able to accurately estimate the position of the target.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

Wang

Liu

et al. 2017

Sensors

Self Cite

View full text Add to dashboard Cite

Received-signal-strength-based (RSS-based) device-free localization (DFL) is a promising technique since it is able to localize the person without attaching any electronic device. This technology requires measuring the RSS of all links in the network constituted by several radio frequency (RF) sensors. It is an energy-intensive task, especially when the RF sensors work in traditional work mode, in which the sensors directly send raw RSS measurements of all links to a base station (BS). The traditional work mode is unfavorable for the power constrained RF sensors because the amount of data delivery increases dramatically as the number of sensors grows. In this paper, we propose a binary work mode in which RF sensors send the link states instead of raw RSS measurements to the BS, which remarkably reduces the amount of data delivery. Moreover, we develop two localization methods for the binary work mode which corresponds to stationary and moving target, respectively. The first localization method is formulated based on grid-based maximum likelihood (GML), which is able to achieve global optimum with low online computational complexity. The second localization method, however, uses particle filter (PF) to track the target when constant snapshots of link stats are available. Real experiments in two different kinds of environments were conducted to evaluate the proposed methods. Experimental results show that the localization and tracking performance under the binary work mode is comparable to the those in traditional work mode while the energy efficiency improves considerably.

show abstract

“…Different from the elliptical model, Hamilton et al [24] proposed a novel inverse area elliptical model (IAEM), which defines that the shadowing effect of a target on a wireless link is inversely proportional to the size of the smallest ellipse that contains the target. Recently, based on extensive experiments or the diffraction theory, the exponential-Rayleigh model (ERM) [25], diffraction model (DM) [26] and saddle surface model (SaS) [27] are proposed to model the RSS changes, and they are all incorporated into the particle filter framework to realize DFL.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Robust Indoor Radio Tomographic Imaging Method in Wireless Sensor Networks

Cao¹,

Ge²,

Ke³

2017

PIER M

View full text Add to dashboard Cite

Abstract-In recent years, radio tomographic imaging (RTI) is an emerging device-free localization (DFL) technology enabling the localization of people and other objects without requiring them to carry any electronic device. Different from other DFL techniques, the RTI method makes use of the changes of received signal strength (RSS) measured on links of the network to estimate the radio frequency (RF) attenuation field and forms an image of the changed field. This image is then used to infer the locations of targets within the deployed network. However, there still lacks an efficient scheme which can achieve robust location estimation performance with low computational cost. To solve this problem, we propose a lightweight robust RTI approach in this paper. The proposed method not only can reduce the algorithm's storage and computational resource requirements, but also exploits the location information of wireless measurement nodes to improve the accuracy of the localization result, which ensures its robust performance. The effectiveness and robustness of the proposed scheme are demonstrated by experimental results where the proposed algorithm yields substantial improvement for localization performance and complexity.

show abstract

A Diffraction Measurement Model and Particle Filter Tracking Method for RSS-Based DFL

Cited by 62 publications

References 34 publications

Small-Scale, Local Area, and Transitional Millimeter Wave Propagation for 5G Communications

Small-Scale, Local Area, and Transitional Millimeter Wave Propagation for 5G Communications

Bayesian Device-Free Localization and Tracking in a Binary RF Sensor Network

A Lightweight Robust Indoor Radio Tomographic Imaging Method in Wireless Sensor Networks

Contact Info

Product

Resources

About