A Framework for UWB-Based Communication and Location Tracking Systems for Wireless Sensor Networks

Ultra wideband (UWB) radio technology is nowadays one of the most promising technologies for medium-short range communications. It has a wide range of applications including Wireless Sensor Networks (WSN) with simultaneous data transmission and location tracking. The combination of location and data transmission is important in order to increase flexibility and reduce the cost and complexity of the system deployment. In this scenario, accuracy is not the only evaluation criteria, but also the amount of resources associated to the location service, as it has an impact not only on the location capacity of the system but also on the sensor data transmission capacity. Although several studies can be found in the literature addressing UWB-based localization, these studies mainly focus on distance estimation and position calculation algorithms. Practical aspects such as the design of the functional architecture, the procedure for the transmission of the associated information between the different elements of the system, and the need of tracking multiple terminals simultaneously in various application scenarios, are generally omitted. This paper provides a complete system level evaluation of a UWB-based communication and location system for Wireless Sensor Networks, including aspects such as UWB-based ranging, tracking algorithms, latency, target mobility and MAC layer design. With this purpose, a custom simulator has been developed, and results with real UWB equipment are presented too

Impact of Mobility on Ranging Estimation using UltraWideband

International audienceThis paper presents a study of the impact of mobility on the distance estimation between 2 nodes of a Wireless Body Area Network (WBAN) by comparing the Two-Way Ranging (2WR) and Three-Way Ranging (3WR) protocols. We consider a WBAN using a typical TDMA-based Medium Access Control (MAC) protocol and a IR-UWB physical layer defined by the standard IEEE802.15.6. We represent the impact by using the Root Mean Square Error (RMSE) in function of two types of parameters, the speed and the time of response. The results show that depending on the speed and the chosen reference point, it is better chose 2WR or 3WR if only mobility is considered. Finally, we propose to extend the study with a joint mobility and clock drift scenario with more sophisticated techniques such as cooperative scheduling algorithms or Aggregated and Broadcast mechanisms

Research on Warehouse Target Localization and Tracking Based on KF and WSN

Abstract: This paper firstly established the model of warehouse targeting and tracking system based on Wireless Sensor Networks (WSN). The principle of Location and tracking is based on the maximum likelihood estimation method of multilateral measurement. According to monitoring motion trajectory of the same unknown target node within a continuous period of time, the motion equation can be established. It can achieve the effective tracking of warehouse target that KF algorithm is applied to carrying out the state estimation of warehouse target motion equation. Simulation results show that, while the warehouse target tracking system state equations are linear, using KF algorithm can obtain satisfactory tracking accuracy. Copyright © 2014 IFSA Publishing, S. L

From the Characterization of Ranging Error to the Enhancement of Nodes Localization for Group of Wireless Body Area Networks

International audienceTime-based localization in Wireless Body Area Networks (WBANs), has attracted growing research interest for the last past years. Nodes positions can be estimated based on peer-to-peer radio transactions between devices. Indeed, the accuracy of the localization process could be highly affected by different factors , such as the WBAN channels where the signal is propagating through, as well as the nodes mobility that bias the peer-to-peer range estimation, and thus, the final achieved localization accuracy. The goal of this paper consists in characterizing the impact of mobility and WBAN channel on the ranging and localization estimation, based on real mobility traces acquired through a motion capture system. More specifically, the ranging error is evaluated over all the WBANs links (i.e. on-body, off-body and body-to-body links), while an impulse Radio Ultra-Wideband (IR-UWB) physical layer, as well as a TDMA-based Medium Access Control (MAC) are playing on. The simulation results show that the range measurement error can be modeled as a Gaussian distribution. To deal with the gaus-sianity observation of ranging error and to provide high positioning accuracy, an adjustable extended Kalman Filter (EKF) is proposed

Efficient DS-UWB MUD Algorithm Using Code Mapping and RVM

A hybrid multiuser detection (MUD) using code mapping and a wrong code recognition based on relevance vector machine (RVM) for direct sequence ultra wide band (DS-UWB) system is developed to cope with the multiple access interference (MAI) and the computational efficiency. A new MAI suppression mechanism is studied in the following steps: firstly, code mapping, an optimal decision function, is constructed and the output candidate code of the matched filter is mapped to a feature space by the function. In the feature space, simulation results show that the error codes caused by MAI and the single user mapped codes can be classified by a threshold which is related to SNR of the receiver. Then, on the base of code mapping, use RVM to distinguish the wrong codes from the right ones and finally correct them. Compared with the traditional MUD approaches, the proposed method can considerably improve the bit error ratio (BER) performance due to its special MAI suppression mechanism. Simulation results also show that the proposed method can approximately achieve the BER performance of optimal multiuser detection (OMD) and the computational complexity approximately equals the matched filter. Moreover, the proposed method is less sensitive to the number of users

3D machine vision system for robotic weeding and plant phenotyping

The need for chemical free food is increasing and so is the demand for a larger supply to feed the growing global population. An autonomous weeding system should be capable of differentiating crop plants and weeds to avoid contaminating crops with herbicide or damaging them with mechanical tools. For the plant genetics industry, automated high-throughput phenotyping technology is critical to profiling seedlings at a large scale to facilitate genomic research. This research applied 2D and 3D imaging techniques to develop an innovative crop plant recognition system and a 3D holographic plant phenotyping system. A 3D time-of-flight (ToF) camera was used to develop a crop plant recognition system for broccoli and soybean plants. The developed system overcame the previously unsolved problems caused by occluded canopy and illumination variation. Both 2D and 3D features were extracted and utilized for the plant recognition task. Broccoli and soybean recognition algorithms were developed based on the characteristics of the plants. At field experiments, detection rates of over 88.3% and 91.2% were achieved for broccoli and soybean plants, respectively. The detection algorithm also reached a speed over 30 frame per second (fps), making it applicable for robotic weeding operations. Apart from applying 3D vision for plant recognition, a 3D reconstruction based phenotyping system was also developed for holographic 3D reconstruction and physical trait parameter estimation for corn plants. In this application, precise alignment of multiple 3D views is critical to the 3D reconstruction of a plant. Previously published research highlighted the need for high-throughput, high-accuracy, and low-cost 3D phenotyping systems capable of holographic plant reconstruction and plant morphology related trait characterization. This research contributed to the realization of such a system by integrating a low-cost 2D camera, a low-cost 3D ToF camera, and a chessboard-pattern beacon array to track the 3D camera\u27s position and attitude, thus accomplishing precise 3D point cloud registration from multiple views. Specifically, algorithms of beacon target detection, camera pose tracking, and spatial relationship calibration between 2D and 3D cameras were developed. The phenotypic data obtained by this novel 3D reconstruction based phenotyping system were validated by the experimental data generated by the instrument and manual measurements, showing that the system has achieved measurement accuracy of more than 90% for most cases under an average of less than five seconds processing time per plant