Rich Multipath Environment Research Articles

Understanding the Diffraction Model in Static Multipath-Rich Environments for WiFi Sensing System Design

Understanding the Diffraction Model in Static Multipath-Rich Environments for WiFi Sensing System Design

Reflection-enabled HDOP-weighted underwater acoustic passive localization with a small aperture array

This paper addresses the localization problem of a noncooperative source in the multipath-rich environment. Unlike conventional schemes that only use the line-of-sight (LOS) and the once-surface-reflected (S1B0) path, the proposed scheme utilizes the complete spatial diversity of all multipaths by quantifying their positioning confidence. In the proposed scheme, all the measurement metrics (angle, relative delay and amplitude) extracted from multipath arrival structure, are combined to achieve localization with a small aperture array.Specifically, we first propose an algorithm for extracting multipath arrival structure based on spatial time-frequency analysis of underwater multipath signals. In this algorithm, each path is iteratively extracted, and the extraction error is close to the Cramér–Rao lower bound (CRLB). Then, the obtained CRLB is used to derive horizontal dilution of precision (HDOP) to quantify the positioning confidence of each path. Finally, the source location is estimated from an optimal HDOP-weighted multipath localization algorithm that assigns different weights to the LOS path and non-line-of-sight (NLOS) paths.Statistical-channel-model-based simulations and lake trial corroborate that the proposed scheme reduces the localization error by at least 128% compared with angle-only, relative-delay-only, and conventional hybrid metrics localization schemes.Index Terms--Underwater acoustic passive localization; Multipath arrival structure; Horizontal dilution of precision; Hybrid metrics.

Single-Target Real-Time Passive WiFi Tracking

Device-free human tracking is an essential ingredient for ubiquitous wireless sensing. Recent passive WiFi tracking systems face the challenges of inaccurate separation of dynamic human components and time-consuming estimation of multi-dimensional signal parameters. In this work, we present a scheme named WiFi Doppler Frequency Shift (WiDFS), which can achieve single-target real-time passive tracking using channel state information (CSI) collected from commercial-off-the-shelf (COTS) WiFi devices. We consider the typical system setup including a transmitter with a single antenna and a receiver with three antennas; while our scheme can be readily extended to another setup. To remove the impact of transceiver asynchronization, we first apply CSI cross-correlation between each RX antenna pair. We then combine them to estimate a Doppler frequency shift (DFS) in a short-time window. After that, we leverage the DFS estimate to separate dynamic human components from CSI self-correlation terms of each antenna, thereby separately calculating angle-of-arrival (AoA) and human reflection distance for tracking. In addition, a hardware calibration algorithm is presented to refine the spacing between RX antennas and eliminate the hardware-related phase differences between them. A prototype demonstrates that WiDFS can achieve real-time tracking with a median position error of 72.32 cm in multipath-rich environments.

Frequency Error Compensation of Unsynchronized Bistatic CW- MIMO Radar for Multiple Human-Body Localization.

This article presents and experimentally evaluates a frequency error elimination technique suitable for unsynchronized bistatic Multiple-Input Multiple-Output (MIMO) radar for human-body detection. First, a mathematical expression of human-body localization using bistatic MIMO radar is presented. Then the direct path is used to eliminate the phase error created by the frequency difference between the transmitter and receiver. A new Doppler-shifted component of the MIMO channel without phase error is derived, and the locations of the multiple targets are calculated by the 2-dimensional MUltiple SIgnal Classification (MUSIC) method. Next, the results of simulations that examine frequency error versus power ratios are discussed to illustrate the effectiveness of the proposed method. An experiment is carried out in an indoor multipath-rich environment. To emulate the unsynchronized condition, the transmitter and receiver use independent Signal Generators (SGs). One to six targets are tested. The experiments demonstrate that our unsynchronized radar system can identify the locations of multiple targets with high accuracy.

CSI:DeSpy

Recently, the spy cameras spotted in private rental places have raised immense privacy concerns. The existing solutions for detecting them require additional support from synchronous external sensing or stimulus hardware such as on/off LED circuits, which require extra obligations from the user. For example, a user needs to carry a smartphone and laboriously perform preset motions (e.g., jumping, waving, and preplanned walking pattern) for synchronous sensing of acceleration signals. These requirements cause considerable discomfort to the user and limit the practicability of prevalent solutions. To cope with this, we propose CSI:DeSpy, an efficient and painless method by leveraging video bitrate fluctuations of the WiFi camera and the passively obtained Channel States Information (CSI) from user motion. CSI:DeSpy includes a self-adaptive feature that makes it robust to detect motion efficiently in multipath-rich environments. We implemented CSI:DeSpy on the Android platform and assessed its performance in diverse real-life scenarios, namely; (1) its reliability with the intensities of physical activities in diverse multipath-rich environments, (2) its practicability with activities of daily living, (3) its unobtrusiveness with passive sensing, and (4) its robustness to different network loads. CSI:DeSpy attained average detection rates of 96.6%, 96.2%, 98.5%, and 93.6% respectively.

Ultrawideband Tomographic Imaging in Multipath-Rich Environment

This letter studies the problem of ultrawideband (UWB) tomographic imaging for unknown building layouts where the multipath-rich condition is considered. Specifically, first, the multiple propagation paths of the UWB signal are analyzed, and a delay estimation algorithm is proposed to estimate the direct path (DP) delay from the multipath signal. Then, a tomographic projection model is established by mapping the relationship between the delay of the DP and the relative permittivity of the unknown layout. Besides, a modified total variation method is developed to reconstruct the building layout, which shows significant performance in preserving the edges of the structure. Finally, the effectiveness of the proposed algorithm is verified using both simulated and real data.

Two-Dimensional RSSI-Based Indoor Localization Using Multiple Leaky Coaxial Cables With a Probabilistic Neural Network

Received signal strength indicator (RSSI) based indoor localization technology has its irreplaceable advantages for many location-aware applications. It is becoming obvious that in the development of fifth-generation (5G) and future communication technology, indoor localization technology will play a key role in location-based application scenarios including smart home systems, manufacturing automation, health care, and robotics. Compared with wireless coverage using conventional monopole antenna, leaky coaxial cables (LCX) can generate a uniform and stable wireless coverage over a long-narrow linear-cell or irregular environment such as railway station and underground shopping-mall, especially for some manufacturing factories with wireless zone areas from a large number of mental machines. This paper presents a localization method using multiple leaky coaxial cables (LCX) for an indoor multipath-rich environment. Different from conventional localization methods based on time of arrival (TOA) or time difference of arrival (TDOA), we consider improving the localization accuracy by machine learning RSSI from LCX. We will present a probabilistic neural network (PNN) approach by utilizing RSSI from LCX. The proposal is aimed at the two-dimensional (2-D) localization in a trajectory. In addition, we also compared the performance of the RSSI-based PNN (RSSI-PNN) method and conventional TDOA method over the same environment. The results show the RSSI-PNN method is promising and more than 90% of the localization errors in the RSSI-PNN method are within 1 m. Compared with the conventional TDOA method, the RSSI-PNN method has better localization performance especially in the middle area of the wireless coverage of LCXs in the indoor environment.

Location Estimates From Channel State Information via Binary Programming

Recent years have witnessed a marked increase in the efficacy and speed to solution of binary program solvers. Leveraging these developments, we propose converting the sparse modelling problem to a binary program, and we derive the conditions under which such a conversion yields the optimal signal support. Furthermore, we derive an upper bound on this condition, which we can minimize by designing a structured tight frame as the dictionary. We apply these theoretical insights to binary optimization of angle of arrival and time of flight estimation (BOAT) using channel state information (CSI), with the goal of localizing a transmitter in an indoor multipath-rich environment. Finally, we show how a covariance array processing approach, together with the novel binary programming paradigm, results in a system that outperforms two state-of-the-art solutions. Our claims are substantiated using both simulated data and experimental data collected using “off-the-shelf” WiFi IEEE 802.11ac routers.

Multipath Mitigation via Synthetic Aperture Beamforming for Indoor and Deep Urban Navigation

A synthetic aperture navigation (SAN) system that exploits downlink cellular long-term evolution (LTE) signals and an inertial measurement unit (IMU) is developed. The system is suitable for multipath-rich environments, such as indoors and deep urban canyons. The proposed SAN system mitigates multipath via a spatial discriminator, which utilizes the motion of a single antenna element to synthesize a geometrically separated antenna array from time-separated snapshots, alleviating the need for a physical antenna array. Signals from the synthesized antenna array are used to beamform towards the line-of-sight (LOS) LTE direction, while suppressing multipath components. Different stages of the beamforming process are discussed, and the computational complexity of the proposed system is analyzed. To deal with the unknown clock biases of the LTE eNodeBs, two navigation frameworks are developed: (1) base/rover and (2) standalone rover. The proposed SAN system is validated experimentally, and the navigation solutions achieved from the following systems are compared: (i) IMU only, (ii) LTE only, (iii) feedforward LTE-SAN, (iv) feedback LTE-SAN, (v) LTE-IMU, and (vi) feedback LTE-SAN-IMU. In the performed experiment, a pedestrian-mounted receiver navigated indoors for 109 m in 50 seconds, while receiving LTE signals from 5 LTE eNodeBs. The proposed LTE-SAN-IMU system exhibited a two-dimensional position root mean-squared error (RMSE) of 1.44 m with a standard deviation of 1.85 m.

Circularly polarized offset-fed DRA elements & their application in compact MIMO antenna

In this work, the generation of circular polarization (CP) in two different dielectric resonator antenna (DRA) elements (one element gets energized by the offset conformal-strip and another one is by the offset microstrip-slot) is precisely explored. CP radiations from the presented DRA elements are acquired by removing a proper asymmetrical notch from the rectangular dielectric resonators (rDRs) and feeding the rDRs at an appropriate offset distance from the center. Furthermore, these two DRA elements are integrated into one antenna system, which can be used for multiple-input-multiple-output (MIMO) application. Owing to the use of two different excitation schemes, the dielectric resonators are mounted on two opposite sides of the substrate and are being radiated in two opposite directions to realize pattern diversity compact MIMO system. So, the presented MIMO design is able to diminish the intersecting spatial field components between the antenna elements, which in turn reduce the envelope correlation coefficient and improve the inter-port isolation. The proposed diversity/MIMO antenna can be utilized as a proficient contender in rich multipath environments to overcome the appearances of channel fading. Experimental outcomes convey that the proposed MIMO antenna features overlapping 3-dB axial ratio (AR) band ranging from 5.30 to 5.87 GHz and overlapping 10-dB impedance band ranging from 5.15 to 6.12 GHz with inter-port isolation of greater than 17 dB throughout the usable frequency band.

A 1.25 μJ per Measurement Ultrasound Rangefinder System in 65 nm CMOS for Explorations With a Swarm of Sensor Nodes

This paper presents an ultrasound rangefinder system able to find relative distances among energy-constrained sensor nodes. The nodes build a swarm that is operated in collision and multipath rich environments. A new distance measurement technique combining Wake-up and Frequency Modulated Continuous Wave (FMCW) is proposed to enable the ranging while neglecting the echoes from passive reflectors in the environment. The building blocks of the sensor nodes comprise a transmitter, a wake-up receiver, and a ranging receiver, all implemented in a 65 nm CMOS technology. The transmitter includes two switched-capacitor converters and an output multiplexer to generate a four-level driving signal and broadcast either a wake-up sequence or a digitally synthesized ultrasound Chirp. The transmitter dissipates $0.43~\mu \text{J}$ and ${0.82~\mu \text {J}}$ to broadcast the wake-up signal and the Chirp, respectively. A mixer first architecture is exploited in the wake-up receiver to reduce the always-on power consumption of the nodes. The ranging receiver uses a heterodyne architecture suited for the FMCW. The power consumption of the wake-up receiver and ranging receiver is 23.6 nW and $0.56~\mu \text{W}$ , respectively. The proposed rangefinder is experimentally characterized up to a 1 m distance in air and dissipates $1.25~\mu \text {J}$ per measurement, achieving a resolution of 18.7 mm at 0.55 m.

RfLoc: A Reflector-Assisted Indoor Localization System Using a Single-Antenna AP

Could we use a single access point (AP) or even an AP equipped with only a single antenna to provide high-accuracy localization service comparable to that of advanced systems using multiple APs or antenna arrays in indoor environments? To answer this question, we present RfLoc, a novel indoor localization system that enables submeter localization accuracy using a single-antenna AP in this article. To this end, RfLoc introduces a custom-designed active reflector with a simple circuit, which can create reflection paths to assist localization. Since our system does not require time synchronization between the target and AP, the absolute time-of-flight (ToF) is unavailable for localization. Instead, we use the relative ToF among the paths that are close to the ground truth to localize the target. The insight behind RfLoc is to use the designed reflector to create paths in a controllable manner, including the control of their strength and propagation delay. The created paths thus are remarkable in multipath-rich environments and can always be used for localization. Then, we employ a sparse recovery-based method, the orthogonal matching pursuit (OMP), to accurately estimate these path ToFs. With ToF estimates, the proposed two-step target localization algorithm is used to solve the target location, including coarse localization based on the semidefinite programming (SDP) and refined localization based on the least squares (LS). We prototype RfLoc using a software-defined radio platform with orthogonal frequency division multiplexing (OFDM) signal, which incorporates a standard WiFi baseband operating. Extensive experimental results show that it can achieve a median localization accuracy of 0.74 m with 100-MHz signal bandwidth. With the advantage of high localization accuracy and easy deployment, RfLoc could enable many Internet-of-Things (IoT) applications.

Measuring the Impact of Beamwidth on the Correlation Distance of 60 GHz Indoor and Outdoor Channels

Due to narrow beamwidth and channel sparseness, millimeter-wave receivers will detect much less multipath than their microwave counterparts, fundamentally changing the small-scale fading properties. By corollary, the de facto Rayleigh-Rice model, which assumes a rich multipath environment interpreted by the Clarke-Jakes omnidirectional ring of scatterers, does not provide an accurate description of this fading nor of the correlation distance that it predicts. Rather, a model interpreted by a directional ring of scatterers, recently proposed in seminal work by Va et al., theoretically demonstrated a strong dependence of correlation distance on beamwidth. To support Va's model through actual measurement, we conducted an exhaustive measurement campaign in five different environments - three indoor and two outdoor - with our 60 GHz 3D double-directional channel sounder, compiling over 36,000 channel captures. By exploiting the super-resolution capabilities of the channel sounder, we were the first, to our knowledge, to measure correlation distance as a function of continuous beamwidth. We showed that for narrow beamwidth, correlation was maintained for much longer distances than predicted by the Rayleigh-Rice model, validating Va's model. As the beamwidth approached omnidirectionality, with increasing number of multipath detected, the behavior indeed approached the Rayleigh-Rice model.

GNSS Adaptive Beamforming Including Null-steering for Unwanted Signal

Unwanted signal such as spoofing, jamming, and multipath is one of the major error factors in GNSS positioning. The purpose of this research is to mitigate the unwanted signal especially multipath error using an array antenna with six elements. We verified the effectiveness of the DCMP (Directionally Constrained Minimization of Power) algorithm to mitigate the unwanted signal. DCMP algorithm is one of the signal synthesis methods using the array antenna and can direct the ``Null'' to the unwanted signal direction by setting some constraint conditions. Testing in multipath-rich environment showed that the positioning accuracy was improved by constraining the ``Null'' toward the building which reflected GNSS signal.

A State-Based Method to Simultaneously Reduce Cycle Slips and Noise in Coherent GNSS-R Phase Measurements From Open-Loop Tracking

The carrier phase measurements of coherent Global Navigation Satellite Systems Reflectometry (GNSS-R)signals have demonstrated the potential for high-accuracy altimetry applications. However, the coherent components in the reflected signal are often accompanied by noncoherent scattered signals, which may cause numerous cycle slips in the conventional open-loop (OL) tracking and postprocessing approach. These cycle slips can lead to meter-level errors in the altimetry retrieval. This article presents a simultaneous cycle slip and noise filtering (SCANF) method for dual-frequency GNSS-R phase measurements generated by OL tracking. The performance of this method is demonstrated using real GNSS-R phase measurements recorded by receivers on board Spire’s low-Earth orbit (LEO) satellites. The retrieved sea surface height anomaly (SSHA) has an average root mean square (rms) of 7.3 cm for sea and sea ice surface reflections relative to the mean sea surface (MSS). The technique presented in this article is also applicable to GNSS carrier phase measurements obtained under other challenging conditions, such as signals propagating through ionosphere plasma structures, radio occultation (RO) signals traversing lower troposphere, and multipath-rich environments.

Relacks

The increasing number of embedded, plugged-in radios around homes and offices in everyday objects such as appliances, TVs, and smart speakers provides an excellent opportunity to bring ultra-low-power backscatter connectivity to billions of devices. However, backscatter links suffer from high loss due to two consecutive propagations and have to operate on narrow link-budget margins, which makes them more susceptible to multipath losses in indoor environments. To address this, we propose a closed-loop backscatter system that exploits diversity sources such as communication frequency and transceivers antennas based on the channel metrics to deliver reliable coverage over an entire area. We prototype a backscatter system with Bluetooth Low Energy (BLE) transceivers and BLE compatible tags and deploy it in several multipath rich indoor environments. Our evaluations show that we can successfully communicate with a backscatter tag in a 50m2 indoor area. The proposed algorithm for selecting communication parameters achieves an average 2.7 x success rate compared to the random selection while satisfying FCC output power requirements for frequency hopping transceivers.

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper.

Wi-Fi For Indoor Device Free Passive Localization (DfPL): An Overview

The world is moving towards an interconnected and intercommunicable network of animate and inanimate objects with the emergence of Internet of Things (IoT) concept which is expected to have 50 billion connected devices by 2020. The wireless communication enabled devices play a major role in the realization of IoT. In Malaysia, home and business Internet Service Providers (ISP) bundle Wi-Fi modems working in 2.4 GHz Industrial, Scientific and Medical (ISM) radio band with their internet services. This makes Wi-Fi the most eligible protocol to serve as a local as well as internet data link for the IoT devices. Besides serving as a data link, human entity presence and location information in a multipath rich indoor environment can be harvested by monitoring and processing the changes in the Wi-Fi Radio Frequency (RF) signals. This paper comprehensively discusses the initiation and evolution of Wi-Fi based Indoor Device free Passive Localization (DfPL) since the concept was first introduced by Youssef et al. in 2007. Alongside the overview, future directions of DfPL in line with ongoing evolution of Wi-Fi based IoT devices are briefly discussed in this paper.

A 2.67 μJ per Measurement FMCW Ultrasound Rangefinder System for the Exploration of Enclosed Environments

This letter presents the design and experimental characterization of an ultrasound rangefinder system. A new distance measurement method is proposed for determining the relative position among sensor nodes that are operated in a collision and multipath rich environment, while needing no common time reference between them. A 65 nm CMOS technology has been used to build the sensor nodes, which comprise an on-chip receiver and transmitter. The proposed rangefinder system is characterized up to 1 m in air and has a range resolution of 6.5 mm, while dissipating $2.67~\mu {\mathrm{ J}}$ per measurement.

Noise Adaptive Optimization Scheme for Robust Radio Tomographic Imaging Based on Sparse Bayesian Learning

This paper addresses the robust signal reconstruction problem caused by different types of noise in radio tomographic imaging (RTI). Most of the existing reconstruction algorithms are built on the assumption of Gaussian noise, which is not the case for practical RTI systems, especially in indoor multipath environments. To weaken the effect of different types of noise on RTI performance, we propose a noise adaptive optimization scheme with sparse Bayesian learning (SBL). Specifically, we model the noise as a mixture of Gaussians (MoG) distribution, which provides the flexibility for describing the unknown and time-varying RTI noise. We further automatically estimate the MoG model parameters as well as the signal under a SBL framework, which makes the signal reconstruction more robust to the complex noise and even outliers. Experimental results in the context of device-free localization show that the proposed scheme can effectively reduce mislocalization and improve localization accuracy in the rich multipath environments, as compared with state-of-the-art reconstruction methods.