Ultra-wideband Sensor Network Research Articles

Clock Rigidity and Joint Position-Clock Estimation in Ultrawideband Sensor Networks

Joint position and clock estimation is crucial in many wireless sensor network applications, especially in distance-based estimation with time-of-arrival (TOA) measurements. In this work, we study the graph properties under which an ultra-wideband (UWB) sensor network is localizable and clock synchronizable with one round TOA-based timestamp measurements. A novel clock rigidity theory is proposed and its topological condition is proved to have close relationship to bearing rigidity. Based on clock rigidity, a graphical approach for analyzing the joint position and clock problem is investigated. It is shown that a position-clock framework with certain graph properties can be uniquely determined up to some trivial variations corresponding to both position and clock parameters. Simulation results are presented to demonstrate how clock rigidity theory is exercised in clock estimation and joint position-clock estimation.

Ultra Wide Band communication for condition-based monitoring, a bridge between edge and cloud computing

Ultra Wide Band (UWB) wireless communications offer a radically different approach to wireless communication compared to conventional narrow band systems in an industrial context. A lot of industrial applications do not require real-time acquisition and uses low data rates, but some scenarios, such as the condition monitoring systems and the control processes require higher data rates (i.e. tens of kHz). General purpose wireless solutions do not compliant industrial standards. This paper discusses the potential of UWB technology for realizing use cases in industry 4.0 and shows the implementation of an UWB sensor network for machine vibration monitoring that is able to acquire and transmit (6.8Mbit/s data rate) accelerometric measures sampled up to 32KHz with a maximum Packet Loss Rate (PLR) of 2.44%. It also shows as UWB could be a valid instrument to connect cloud computing and edge computing even for real time extraction of diagnostic and prognostic features from high-frequency signals. Different experiments have been performed in an industrial and laboratory environment to evaluate the performance of UWB radio for communication in a wireless sensor network.

Online Target Localization Using Adaptive Belief Propagation in the HMM Framework

This paper proposes a novel adaptive sample space-based Viterbi algorithm for target localization in an online manner. The method relies on discretizing the target's motion space into cells representing a finite number of hidden states. Then, the most probable trajectory of the tracked target is computed via dynamic programming in a Hidden Markov Model (HMM) framework. The proposed method uses a Bayesian estimation framework which is neither limited to Gaussian noise models nor requires a linearized target motion model or sensor measurement models. However, an HMM-based approach to localization can suffer from poor computational complexity in scenarios where the number of hidden states increases due to high-resolution modeling or target localization in a large space. To improve this poor computational complexity, this paper proposes a belief propagation in the most probable belief space with a low to high-resolution sequentially, reducing the required resources significantly. The proposed method is inspired by the k-d Tree algorithm (e.g., quadtree) commonly used in the computer vision field. Experimental tests using an ultra-wideband (UWB) sensor network demonstrate our results.

Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

Accurate localization in indoor environments with ultra-wideband (UWB) technology has long attracted much attention. However, due to the presence of multipath components or non-line of sight (NLOS) propagation of the radio signals, it has been converted to a critical challenge. Existing solutions use many fixed anchors in the indoor environment. Particularly, large areas require many anchor points and in the case of unexpected events that lead to the destruction of existing infrastructures, the fixed anchor points cannot be used. In this paper, a novel localization framework based on the transmitting signal from a mobile UWB sensor on the outside of the building and its received signal regarding the modified Saleh Valenzuela (SV) channel model is presented. After preprocessing the received signals, two new procedures to reduce the ranging error caused by multipath components are proposed. In the first procedure, two machine learning algorithms including multi-layer perceptron (MLP) and support vector machine (SVM) using the extracted features from the received UWB signal time and power vectors are implemented. Moreover, in the second procedure, two deep learning algorithms including MLP and convolutional neural networks (CNNs) using the received UWB signal time and power vectors are implemented to improve the performance of the indoor localization system. The simulation results show that the architecture designed for the convolutional neural network based on the hybrid dataset (the combination of the dataset related to received UWB signal time and power vectors) provides a mean absolute error (MAE) of about 3 cm.

Towards Industrial Ultra-Wideband Networks: Experiments for Machine Vibration Monitoring

We discuss the potential of ultra-wideband (UWB) technology for realizing use cases in industry 4.0 and demonstrate the implementation of an UWB sensor network with off-the-shelf transceivers for machine vibration monitoring. We propose a flexible medium access layer tailored for industrial sensing and show how the choice of its parameters is affected by the propagation environment and hardware constraints. Our real-world experiments reveal that links up to 20 meters can be achieved in industrial environments with an average packet loss rate in the order of 10 -6 for typical links. A comparison with a commercial vibration monitoring system based on IEEE 802.15.4 spread spectrum shows that UWB attains a twenty-times higher throughput for similar link ranges.

Low Profile Sinuous Slot Antenna for UWB Sensor Networks

This article describes the design and implementation of a low-profile sinuous slot antenna, intended for ultra-wideband (UWB) sensor networks, which can be produced on one conductive layer. The article explains the design and optimization of the sinuous slot antenna and its modifications, including its sinusoidal curve shape. Other modifications were aimed at optimizing the antenna feeding. Desirable properties of the designed and implemented antenna modifications were verified both by simulation and empirically. Experimental measurements of the antenna’s properties were carried out using a vector network analyzer in an anechoic chamber and also by a pulsed UWB radar in the frequency range from 0.1 to 6 GHz. The low-profile antennas were implemented on a Rogers RO3206 substrate.

Multiple moving person tracking by UWB sensors: the effect of mutual shielding persons and methods reducing its impacts

Ultra-wideband (UWB) radars are sensors allowing to track people in critical environments and situations. The results reached by single UWB sensors for such applications have shown that they are able to detect and track a person very well in a single person scenario. However, in multiple moving person scenarios, the ability of a single UWB sensor to detect several persons is usually significantly reduced. This is caused by a mutual shielding among people. In this paper, we will deal with the mutual shielding effect and its impacts, as well as with the methods of improving multiple moving person tracking by UWB radars. Firstly, we will provide a comprehensive description of the mutual shielding effect. Then, based on its analyses, we will state three complementary approaches created by the authors of this paper to reduce its impacts. They include an enhancement of the low-level echo of the targets, radar antenna array positioning at a convenient height, UWB sensor network application and, finally, their mutual combinations. The properties of those approaches will be demonstrated by two experimental measurements aimed at through wall tracking of two and three people, respectively. The results obtained in the experiments will illustrate the mutual shielding effect and the potential of the methods we have proposed to reduce its impacts.

Towards robust and efficient device-free localization using UWB sensor network

Device-free localization (DFL) is the method of using distributed wireless sensors to localize the target without carrying any devices. Existing DFL methods leverage the variation of narrowband received signal strength (NRSS) which is vulnerable to multipath fading, and thus results in considerable performance degradation in indoor environments. Moreover, the inefficient sensor deployment of traditional DFL involves huge human efforts, which is not suitable for emergency scenarios. In this paper, we utilize sensors transmitting ultra-wideband (UWB) signals to solve both problems. We proposed two RSS variation estimation methods based on the channel impulse response (CIR) measurements provided by UWB sensors, which turn out to be more robust to multipath than NRSS due to the fine multipath resolution of UWB signals. We also employ a higher operating frequency to enhance the shadowing loss for mitigating the multipath effect. Additionally, satisfactory sensor self-localization is achieved under the cooperative localization framework owing to the accurate ranging capability of UWB sensors. We conducted experiments in three different environments to explore the feasibility of our method. The results show that the proposed method gains much better localization performance and requires less human efforts than narrowband DFL.

Vivaldi Antenna for UWB Sensor Networks

In this paper, the Vivaldi antenna designed for ultra-wideband (UWB) sensor networks by exponential curves is presented. Manufactured antennas are used for through-wall measurements. The article describes the design and optimization of the antenna, antenna measurements using the network vector analyser and pulse radar. Measured results are compared with simulated results. The antenna is also compared with commercially manufactured antennas used in the above mentioned applications. The designed Vivaldi antenna is printed on ARLON 600 substrate and operates in the frequency band from 0.810 GHz up to more than 12 GHz. Experimental results show good agreement with the simulated performance.DOI: http://dx.doi.org/10.5755/j01.eie.22.4.15915

A Bernoulli Filter for Extended Target Tracking Using Random Matrices in a UWB Sensor Network

In this paper, we propose a new tractable Bernoulli filter based on the random matrix framework to track an extended target in an ultra-wideband (UWB) sensor network. The resulting filter jointly tracks the kinematic and shape parameters of the target and is called the extended target Gaussian inverse Wishart Bernoulli (ET-GIW-Ber) filter. Closed form expressions for the ET-GIW-Ber filter recursions are presented. A clustering step is inserted into the measurement update stage in order to have a computationally tractable filter. In addition, a new method that is consistent with the applied clustering method is embedded into the filter recursions in order to adaptively estimate the time-varying number of measurements of the extended target. The simulation results demonstrate the robust and effective performance of the proposed filter. Furthermore, real data collected from a UWB sensor network are used to assess the performance of the proposed filter. It is shown that the proposed filter yields a very promising performance in estimation of the kinematic and shape parameters of the target.

Achieving Distributed Consensus in UWB Sensor Networks: A Low Sampling Rate Scheme with Quantized Measurements

Distributed consensus in sensor networks has received great attention in the last few years. Most of the research activity has been devoted to study the sensor interactions that allow the convergence of distributed consensus algorithms toward a globally optimal decision. On the other hand, the problem of designing an appropriate radio interface enabling such interactions has received little attention in the literature. Motivated by the above consideration, in this work an ultrawideband sensor network is considered and a physical layer scheme is designed, which allows the active sensors to achieve consensus in a distributed manner without the need of any admission protocol. We focus on the class of the so-called quantized distributed consensus algorithms in which the local measurements or current states of each sensor belong to a finite set. Particular attention is devoted to address the practical implementation issues as well as to the development of a receiver architecture with the same performance of existing alternatives based on an all-digital implementation but with a much lower sampling frequency on the order of MHz instead of GHz.

Fiber-connected UWB sensor network for high-resolution localization using optical time-division multiplexing

A fiber-connected ultra-wideband (UWB) sensor network for high-resolution localization which consists of a central station and several sensor nodes is proposed and demonstrated. To make the central station easily identify the received UWB pulses from different sensor nodes, optical time-division multiplexing (OTDM), realized by inserting a certain length of optical fiber between every two sensor nodes, is implemented. Due to the OTDM technology, the UWB pulses received by different sensors are mapped into different time slots, so neither parameter estimation nor clock synchronization is required in the UWB sensor node. All complex signal processing is completed in the central station, which greatly improve the localization accuracy and simplify the system. A proof-of-concept experiment for two-dimensional localization is demonstrated. Spatial resolution as high as 3.9 cm is achieved.

Ultra-wide band sensor networks in oil and gas explorations

Seismic exploration and monitoring for oil and gas reservoirs is a peculiar application that requires a large number (1000-2000 nodes/sqkm) of geophone sensors deployed outdoors over large areas (≥ 40 sqkm) to measure backscattered wave fields from artificial sources. A storage/processing unit or sink node collects the measurements from all the geophones to obtain an image of the sub-surface. The existing cabling system to connect sensors is known to cause inefficiencies, large logistic and weight costs, as well as insufficient flexibility in survey design. Oil companies are therefore expecting that wireless connectivity will provide the enabling technology for future seismic explorations. This application represents a new challenging research area for the wireless community. Early results suggest that current off-the-shelf radio solutions do not guarantee the minimum requirements in terms of system usability and energy consumption, not even for current deployment size. This article presents a tutorial view to introduce the basic principles of seismic acquisition systems that are necessary to define the wireless geophone network specifications. Strict sampling synchronization constraint over large geographic areas, high precision sensor localization, and high data rate are all requirements calling for a scalable network system where Ultra-Wide Band radio transmissions play a key role as the only viable technology.

A Cooperative Localization Algorithm for UWB Indoor Sensor Networks

This paper deals with range-based localization in ultra wideband sensor networks, allowing for the possibility of large range measurement errors because of a failure to detect the direct paths between some nodes. A novel algorithm is proposed that uses only partial knowledge of the service area topology, particularly of the positions of objects which are capable of causing undetected direct path (UDP) propagation conditions. Although the spirit of the proposed approach, because of the lack of information on the range error statistics, is to remove measurements performed under UDP conditions from the computation of the location estimate, these measurements are used implicitly by the algorithm to contribute to the erroneous trial locations being discarded. A cooperative stage is included that allows the probability of localization of a target with an insufficient initial number of accurate range measurements to increase. The proposed algorithm outperforms a variety of alternative positioning techniques, and thus illustrates the capability of this topology knowledge to mitigate the UDP problem, even in the absence of any knowledge about the range error statistics.

Simulated Annealing Mechanic Based Noncoherent Signal Detection for Ultra-wideband Sensor Networks

Ultra-wideband (UWB) sensors have extensive commercial and military applications. Unfortunately, coherent signal detection in the presence of intensive multipath propagations may generally become impractical due to complicated realization algorithms and hardware requirements. In this article, we deal with noncoherent UWB signal detection within a promising biological framework, which can also be generalized to the binary-hypothesis target-detection problem, i.e. identification of the presence or absence of a target. Through the developed characteristic representations, signal detection is firstly formulated as a two-group pattern (or target) classification problem in a 2-D feature plane, in which an optimal decision bound can be numerically derived given the supervised training instances. This optimization problem is addressed by using the nature-inspired simulated annealing algorithm (SA), which essentially emulates the physical annealing process of forming the freeze state with the minimum energy. In sharp contrast to traditional optimization techniques, by probabilistically permitting search movement towards worse solutions, SA algorithm can converge to the global optimal with an asymptotical probability of 1. The numerically derived detection performance demonstrated that our present technique is much superior to the existing noncoherent schemes, which provides the appealing signal/target detection architecture for the emerging UWB sensor networks.

Threshold Selection for TOA Estimation based on Skewness and Slope in Ultra-wideband Sensor Networks

Accurate localization has gained significant interest within sensor networks but because of the high sampling rate, Matched Filter (MF) based coherent Time of Arrival (TOA) estimation algorithms are not practical for low cost, low complexity Ultra-wideband (UWB) ranging, positioning and tracking systems. In this paper, an Energy Detection (ED) based non-coherent TOA estimation algorithm is presented where the TOA is estimated via Threshold Crossing (TC). The expected values of skewness, maximum slope, kurtosis and standard deviation with respect to the Signal to Noise Ratio (SNR) are investigated using the IEEE 802.15.4a Channel Models (CM). It is shown that the skewness and maximum slope are more sensitive to the SNR and thus more suitable for TOA estimation. To improve the precision of ED based TOA estimation, especially in low SNR environments, a new threshold selection algorithm is proposed which is based on a joint metric of the skewness and maximum slope. The best threshold values for different SNRs are investigated and the effects of integration period and channel models are examined. Comparisons with other ED based algorithms show that in both the CM1 and CM2 channels, the joint metric provides higher precision and robustness in both high and low SNR environments.

NLOS error mitigation with information fusion algorithm for UWB ranging systems

This article puts forward a scalar weighting information fusion (IF) smoother with modified biased Kalman filter (BKF) and maximum likelihood estimation (MLE) to mitigate the ranging errors in ultra wide band (UWB) systems. The information fusion algorithm uses both the time of arrival (TOA) and received signal strength (RSS) measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE 802.15.4a acts as a multi-sensor system with multi-scale sampling. Then the scalar-based IF smoother accurately estimates the range measurement in the line of sight (LOS) and non-line of sight (NLOS) condition of UWB sensor network, during which the effectiveness of the IF in mitigating errors is especially focused during the LOS/NLOS transitions. Simulation results show that the proposed hybrid TOA-RSS fusion approach indicates a performance improvement compared with the usual TOA-only and other IF method, and the estimated ranging metrics can be used for achieving higher accuracy in location estimation and target tracking.

Probability of early detection of ultra-wideband positioning sensor networks

Ultra-wideband (UWB) sensor networks can be efficiently applied to collect data through sensors deployed for preserving environment and rescuing lives. The high-precision positioning capabilities of UWB technology, based on its operation of ultra-short data bearing pulses, can be a reliable solution for such applications. Most of the UWB positioning techniques are based on time-of-arrival of the first path which can be effectively estimated when the probability of early detection (PoED) is accurately evaluated. In this study, we study the PoED of UWB positioning sensor networks subject to narrowband interference modelled with Nakagami-m distribution. We derive novel closed-form approximate expressions which are simpler and have remarkable accuracy compared to the previously published semi-infinite series expressions. Furthermore, to simplify our approach, we present tight lower and upper closed-form bounds for the PoED. Numerical results and simulations are performed to validate the analytical derivations.

Lifetime Maximization in UWB Sensor Networks for Event Detection

The operational lifetime of a wireless sensor network (WSN) for event detection is determined by the maximum time that the network is able to meet given detection requirements (DRs), i.e., the probabilities of detection and false alarm demanded by the application. In this paper, we address the problem of maximizing lifetime of such WSNs through optimizing quantization and routing of event measurements. In particular, we consider the task of monitoring multiple events and reporting the observations to a sink, whereby sensor nodes adapt their data generation rate and the data flow distribution in the network for the purpose of lifetime maximization. We make use of ultra-wideband (UWB) signaling at the physical layer, which is well-suited for event-detection WSNs, because of the low-energy consumption for data transmission and relative robustness to multiuser interference. Expressing the DRs as convex constraints in the optimization variables, we present a convex-optimization framework for lifetime maximization of event detection UWB-based WSNs. Furthermore, based on the dual decomposition approach, we propose a decentralized algorithm, which makes it possible to solve the lifetime-maximization problem in a distributed manner and thus shares the computational complexity among network nodes and is robust to artifacts like node failures. Numerical results show that the proposed joint adaptation of quantization and routing leads to significant improvements in network operational lifetime compared to benchmark approaches known from literature.

Upper Bounds on Lifetime of Ordinary Clustering Ultra Wide Band Sensor Networks

考虑了En</em> 个传感节点和一个Sink 组成的跳时脉冲无线电超宽带(time hopping impulse radio ultra wideband,简称TH-IR UWB)传感网,其中,n 个传感节点均匀且独立地分布在正方形上.分别推导了常规分簇的密集和扩展TH-IR UWB 传感网的生存期上界.常规分簇网络的簇头仅对本簇内的节点发送的数据进行融合,而对其他簇头发送的数据仅转发.研究结果表明,常规分簇密集TH-IR UWB 传感网的生存期上界与节点密度(或节点数)成正比,扩展网络的生存期上界与部署区域大小(或节点数)成反比.研究结果也表明,对于密集网络,理想情形下的生存期上界比静态网络提高了(<em>n</em>/log<em>n</em>)^1/2 倍;而对于扩展网络,理想情形下生存期的上界比静态网络提高了(<em>λn</em>/log(<em>λn</em>))^1/2 倍.因此,节点或Sink 在正方形内随机移动能够提高传感网生存期.;This paper considers the time hopping impulse radio ultra wide band (TH-IR UWB) sensor networks, with <em>n</em> sensor nodes uniformly and independently located on a square and a Sink. The upper bounds on the lifetime of ordinary clustering dense and extended TH-IR UWB sensor networks are derived respectively. For ordinary clustering networks, the cluster head aggregates data from the nodes within its own cluster, and relays data to other cluster heads. The results indicate that the upper bounds on the lifetime of ordinary clustering dense TH-IR UWB are in proportion with the node density (or the number of nodes), while the upper bounds on the lifetime of extended networks are inversely proportional to the size of the deployment area (or the number of nodes). The results also reveal that the upper bound on the lifetime of dense network in the ideal case are longer than those of the static network by a factor of (<em>n</em>/log <em>n</em>)^1/2, and the upper bound on the lifetime of extended network in the ideal case are longer than those of the static network by a factor of (<em>λn</em>/log(<em>λn</em>))^1/2. Therefore, sensor nodes, or the Sink moving randomly in the square, can improve the lifetime of ordinary clustering TH-IR UWB sensor network.