Wireless sensor network localization is an essential problem that has attracted increasing attention due to wide requirements such as in-door navigation, autonomous vehicle, intrusion detection, and so on. With the a priori knowledge of the positions of sensor nodes and their measurements to targets in the wireless sensor networks (WSNs), i.e. posterior knowledge, such as distance and angle measurements, it is possible to estimate the position of targets through different algorithms. In this contribution, two approaches based on least-squares and Kalman filter are described for localization of one static target in the WSNs with distance, angle, or both distance and angle measurements, respectively. Noting that the measurements of these sensors are generally noisy of certain degree, it is crucial and interesting to analyze how the accuracy of localization is affected by the sensor errors and the sensor network, which may help to provide guideline on choosing the specification of sensors and designing the sensor network. To this end, we make theoretical analysis for the different methods based on three types of measurement noise: bounded noise, uniformly distributed noises, and Gaussian white noises. Simulation results illustrate the performance comparison of these different methods.

Wireless sensor network (WSN) is an important component of a cyberphysical system. Locating node information is a crucial problem for WSN. Currently, distance vector-hop method (DV-Hop), one of popular range-free algorithms, is widely deployed to estimate the location. However, the estimation precision is challenging. In this paper, a new evolutionary algorithm named oriented cuckoo search algorithm (OCS) is designed. In OCS, the global search capability is dominated by the combination of two different random distributions. To provide a deep investigation, ten different random distributions are employed and compared with CEC2013 test suits. Numerical results show the hybrid distribution combined with Lvy distribution and Cauchy distribution achieves the best performance. Furthermore, OCS with this hybrid distribution is also incorporated into the methodology of DV-Hop algorithm to improve the precision performance. Simulation results demonstrate that our modification achieves better precision performance when compared with three other DV-Hop algorithms. Oriented cuckoo search algorithm is designed.Oriented cuckoo search algorithm with different distributions are investigated.CEC2013 28 benchmarks test suits are employed to test and compared with several other algorithms.Oriented cuckoo search algorithm is employed to improve the performance of DV-Hop algorithm.

Wireless location is a key technology for wireless sensor networks (WSN). This paper firstly describes a wireless location system based on nanoLOC chip which can provide both high-accuracy location and data communication. Secondly, we present an indoor location algorithm based on cooperative of state matrix and Kalman filter (State-LKalman), and apply it to the time of arrival(TOA) algorithm of the wireless location system. Simulation results show that the State-LKalman has high accuracy than the cooperative of least square error (LSE) and Kalman filter (LKalman) algorithm. Finally, we describe the design of the hardware and software of the system and do the experiments on the platform. Experimental results show that the system is stable and reliable and can achieve high accuracy location; the cumulative probability of 1.5 meter error can achieve 50%.

Wireless distance measurement techniques based on portable embedded platforms are expected to play a key role in several industrial and domestic applications. In this paper the ranging accuracy of a commercial Chirp Spread Spectrum (CSS) kit is evaluated experimentally in a real-world context. The proposed analysis provides more precise and exhaustive information than what it is usually reported in the technical literature. In fact, this paper is specifically focused on performance evaluation and it deals with the case of short-range indoor scenarios in both Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) repeatable conditions. The resulting analysis represents the first step towards the design of a custom indoor embedded navigation system for a smart rollator assisting impaired people to move safely in an indoor public environment.

Unmanned aerial vehicles (UAVs) have become one of the most popular and promising means for both military and civilian posts and academic research areas. Localization of the UAVs and persistent tracking of a UAV have vital importance to provide a UAV with navigation information and help to cope with getting lost permanently. Indeed, inertial navigation system (INS) and global positioning system (GPS) seem to be adequate for navigation of UAVs. However, an alternative augmented navigation system for UAVs should be taken into consideration since INS has accumulated errors and GPS always has the possibility of jamming and satellite signal loss. Unmanned air systems (UASs) are playing increasingly prominent roles in defense programs and strategy around the world. For many of applications to develop into maturity, the reliability of UASs will need to increase, their capabilities will need to be extended further, their ease of use will need to be improved, and their cost will have to come down. In this paper the fusion between the data derived from the magnetometer, rate-gyro, accelerometer, pitot tube, pressure and the GPS is integrated in one system using an extended kalman filter (EKF) which uses a linear error model to estimate the errors in the states for UAV to enhance its performances. The proposed system introduces a robust navigation based on bistatic radar and bi-sensor for UAVs flight dynamics with decreases the faults in operational environment.

This work presents Semi-Supervised SLAM - a method for developing a map suitable for coarse localization within an underground environment with minimal human intervention, with system characteristics driven by real-world requirements of major mining companies. This work leverages existing information common within a mining environment - namely a surveyed mine map - which is used to sparsely ground map locations within the mine environment, increasing map accuracy and allowing localization within a global frame. Map creation utilizes a low cost camera sensor and minimal user information to produce a map which can be used for single camera localization within a mining environment. We evaluate the localization capabilities of the proposed approach in depth by performing data collection on operational underground mining vehicles within an active underground mine and by simulating occlusions common to the environment such as dust and water. The proposed system is capable of producing maps which have an average localization error 2.5 times smaller than the next best performing method ORB-SLAM2, comparable localization performance to a state-of-the-art deep learning approach (which is not a feasible solution due to both compute and training requirements) and is robust to simulated environmental obscurants.

This paper proposes a localization technique for mobile robots using a wireless sensor network (WSN), based on chirp spread spectrum ranging and an inertial measurement unit (IMU). A discrete-time H∞ filter with input forcing function is newly derived for mobile robot localization. The position of the robot is estimated by the filter using an integration of position information collected by the WSN and absolute acceleration data obtained by the IMU. From the dynamics of the robot, the solution existence of the proposed filter is shown, and a low-complexity computational method to obtain a solution from the filter is proposed by a generalized eigenvector approach. Through simulation and experiments, we evaluate the performance of the proposed H∞ filter and compare it with the standard Kalman filter.

This paper evaluates the performance of the networked munitions communication and localization. The key requirements of networked munitions are proposed and analyze the possible technologies for localization. The hardware for communication and localization are selected and designed, the software scheme are illuminated according to the hardware and the project. The experiments prove that the design is effective and satisfies the application of networked munitions.

The wireless assistance system safe@home builds a wireless sensor network which enables the localisation of a person wearing a tag and to collect the person's vital data. Furthermore it provides the opportunity to connect to home automation elements like EnOcean1 sensors and actuators. The goal to this project is to provide a system which can easily be installed into existing homes and therefore is low-priced, to allow elder people to live safely and independently in their own homes for as long as possible.

The increasing diffusion of mobile and portable devices provided with wireless connectivity makes the problem of distance measurement based on radio-frequency technologies increasingly important for the development of next-generation nomadic applications. In this paper, the performance limitations of two classic wireless ranging techniques based on received signal strength (RSS) and two-way time-of-flight (ToF) measurements, respectively, are analyzed and compared in detail. On the basis of this study, a data fusion algorithm is proposed to combine both techniques in order to improve ranging accuracy. The algorithm has been implemented and tested on the field using a dedicated embedded prototype made with commercial off-the-shelf components. Several experimental results prove that the combination of both techniques can significantly reduce measurement uncertainty. The results obtained with the developed prototype are not accurate enough for fine-grained position tracking in Ambient Assisted Living applications. However, the platform can be successfully used for reliable indoor zoning, e.g., for omnidirectional and adjustable hazard proximity detection. Most importantly, the proposed solution is absolutely general, and it is quite simple and light from the computational point of view. Accuracy could be further improved by using a more isotropic antenna and by integrating the ToF measurement technique at the lowest possible level on the same radio chip used for communication. Usually, this feature is not available in typical low-cost short-range wireless modules, e.g., for wireless sensor networks. Thus, the results of this research suggest that combining RSS with ToF measurements could be a viable solution for chip manufacturers interested in adding ranging capabilities to their radio modules.

The challenge of target tracking is one of the most important applications of WSNs (Wireless Sensor Networks). Traditionally, Kalman filter and its derivatives are some of the most popular algorithms for solving the signal tracking problem. In a WSNs tracking application, the target motion and state update dynamics might be modelled by linear or non-linear structures depending on the specific scenario. This paper compares extended Kalman Filters with the P, PV and PVA dynamics models for object tracking in sensor networks.

The aerial robots represent an interested and rich area of research because they are very useful to perform complex tasks such as localization and tracking targets. To develop blimp system that is appropriate in diversity scenarios, an intelligent control with high autonomy degree is required. Therefore, we design blimp robot based on embedded system; then, we present several fuzzy sets models that should deal with autonomous, navigation and visual tracking problems. These models are empirically designed by combining the possibilities distributions theory with fuzzy logic. Thus, this paper addresses the problem of tracking robots in parallel with achieving the cooperative behavior based on computer vision system and artificial intelligent control to improve the efficiency of such system. In addition, considering use of wireless sensor networks for estimation multi-targets locations and the distances between them is presented.

Robustly and accurately localizing vehicles in underground mines is particularly challenging due to the unavailability of GPS, variable and often poor lighting conditions, visual aliasing in long tunnels, and airborne dust and water. In this paper, we present a novel, infrastructure-less, multisensor localization method for robust autonomous operation within underground mines. The proposed method integrates with existing mine site commissioning and operation procedures and includes both an offline map-building process and an online localization algorithm. The approach combines the strengths of visual-based place recognition, LIDAR-based localization, and odometry in a particle filter fusion process. We provide an extensive experimental validation using new large data sets acquired in two operational Australian underground hard-rock mines (including a 600m-deep multilevel mine with approximately 33 km of mapping data and 7 km of vehicle localization) by actual mining vehicles during production operations. We demonstrate a significant increase in localization accuracy over prior state-of-the-art SLAM research systems and real-time operation, with processing times in the order of 10 Hz. We present results showing a mean error of 0.68 m from the Queensland Mine data set and 1.32 m from the New South Wales Mine data set and at least 86% reduction in error compared with prior state of the art. We also analyze the impact of the particle filter parameters with respect to localization accuracy. Together this study represents a new approach to positioning systems for currently deployed autonomous vehicles within underground mine environments.

Proximity sensors are increasingly used in distributed monitoring applications, e.g. for surveillance purposes. However, most of existing solutions have two different, usually contrasting requirements, i.e. either they have strong directional features, or they just cover very short distances. In order to tackle such issues, in this paper a new, almost omnidirectional proximity detection technique is described. The proposed approach is explicitly conceived for wearable wireless sensor networks (WSN) nodes and it relies on the fusion of time-of-flight (ToF) and received signal strength (RSS) values. Even though it is not accurate enough for fine-grained indoor localization, the proposed solution is robust in detecting when a given threshold is crossed by a moving object. Also, it is flexible, light from the computational point of view and easy to implement using commercial off-the-shelf (COTS) components.

Oriented cuckoo search algorithm (OCS) is a newly proposed variant of cuckoo search algorithm, to avoid the premature convergence, weighted-based oriented cuckoo search (WOCS) is proposed which is a combination of OCS and the standard CS. WOCS achieves a balance between exploration and exploitation. To test the performance, WOCS is incorporated into the methodology of DV-Hop localization algorithm for node localization in wireless sensor networks. The simulation results show that DV-Hop localization with WOCS achieves best positioning accuracy when compared with other two algorithms.

Nowadays, indoor ranging and localization have become necessary in daily life. Due to the multi-path propagation and noise in the indoor environment, phase domain ranging method using multi-frequency has been proposed which achieves accurate estimation of indoor target. However, as the indoor communication is usually carried on Bluetooth Low Energy (BLE) or Zigbee, high efficiency is indispensable in the face of limited bandwidth and measuring time. Thus, in this thesis, we aim to reduce the number of frequencies used in the ranging while keeping an acceptable estimation accuracy. We at first build the signal model and study the ambiguity range of the problem. Then based on the estimation theory and the concept of compressive sensing (CS) theory, we take the Cramer Rao Lower Bound (CRLB) and the matrix coherence as the criteria and select the optimal subset on given tone set. To test the performance of selected subset, we utilize gridless reconstruction algorithms, noiseless global matched filter (NL-GMF) and atomic norm minimization (ANM), to estimate target distance with the subset in both simulated data and real data and provide the mean square error (MSE), the estimation probability and the successful estimation probability in various estimation conditions.

Network entities with synchronized clocks are an enabler for many interesting and innovative applications. Localization of mobile WLAN devices by means of propagation delay measurements and position calculation according to time difference of arrival (TDoA) method is one of the most interesting applications. Those localization techniques are favored over standard signal strength based methods when high accuracy positioning is desired. This paper presents a system that utilizes special synchronized receivers in conjunction with a central unit to locate WLAN devices. The system architecture is described in terms of the integration of all participating entities for both, hardware and software. 1

Localization is a key aspect of emergent applications in the medical, industrial and consumer field. In this article we survey state of the art, identify current challenges and issues for localization systems and suggest a unified layered architecture. The analysis reveals that challenges cannot be addressed in an isolated manner for example, energy consumption is tied to the choice of algorithm and employed hardware. To separate various challenges and investigate them independently, we propose the concept of position providers. Position providers in the lower layers allow abstraction of positioning methods, positioning algorithms and positioning hardware. Thereby, a position provider encapsulates methods, algorithms and hardware. Furthermore, we suggest a classification of position providers inspired by related work. We propose a unified architecture for location systems which uses positioning and integration layers as main building blocks.

Indoor localization is important for medical and industrial application as well as for wireless emergency and security systems. For such applications an accuracy within a few meters is desired. Available radio based systems within that accuracy are neither cost effective nor easy to deploy. In this work, we suggest an approach called bi-phase measurement based on phase measurements with two frequencies to determine the location of a tag. We design and build a complete indoor positioning system based on bi-phase measurements with easy to deploy and mobile wireless sensor nodes. The wireless sensor nodes form anchors and tags and communicate results to a location engine of the indoor positioning system. Our implementation comprises low-cost IEEE802.15.4 radio chips with built-in support for phase measurements unit for both, anchor and tags. We compute the position of the tag based on distance estimation retrieved with biphase measurements. We evaluate our indoor positioning system providing first measurement results for accuracy and precision and discuss trade-off between scalability and accuracy.

Indoor localization is important for logistics, industrial applications and for several consumer applications. In the area of logistics, e.g. warehouses, localization accuracy within a few meters is desired. Available radio based systems within that accuracy are neither cost effective nor easy to deploy. Distance estimation together with triangulation are one of the standard solutions for localization. In this work, we propose phase measurements between two wireless sensor nodes for distance estimation. We introduce a mathematical model to estimate distances from phase measurements with multiple frequencies and provide a systematic analysis of possible sources of errors. Additionally, we derive requirements, e.g. resolution for a phase measurement unit to achieve a given accuracy. We present measurements for evaluation to confirm our theoretical results. Our implementation comprises a low cost IEEE 802.15.4 hardware with a built-in phase measurement unit. We implement the developed algorithm for distance estimation in our wireless sensor networks and use two wireless sensor nodes to perform a phase measurement. The contributions of the paper comprise a new model for phase measurements to estimate distances and a preliminary evaluation with our hardware.