Signal Strength Information Research Articles

Model-Based Navigation Control for Communication-Aware Motion Using Harmonic Potential Fields

This paper suggests an optimal and provably-correct method for inducing communication-aware, context-sensitive mobility in an autonomous agent. The method can fuse crisp representations of arbitrarily shaped hard-obstacles with soft representations of the wireless signal strength field that forms within the confines of the hard-obstacles. The procedure can produce a navigation control signal able to induce in a wide class of realistic agents optimal, communication-aware, dynamically-friendly trajectories to a specified target point in the environment. The approach is developed with the aid of the potential-field method for motion planning. Its immediate goal is to tackle the model-based case where the navigator assumes full knowledge of the obstacles and Wireless Communication Link (WCL). The treatment in this part provides the theoretical foundations and the tools necessary to extend the method to the sensor-based case where the agent does not a-priori know the obstacles or the WCL which have to be sensed online. Theoretical proofs of the communication-aware navigator’s performance are provided and its behavior is verified using intensive realistic simulation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we target applications like search and rescue, where mobile agents may be utilized for information gathering and mapping purposes in regions that are hazardous to humans. Our proposed approach can simultaneously combine obstacle information and signal strength information to generate guidance signals. Trajectories manifested using these guidance signals can avoid regions with very low signal strength. Typically, guidance signals from a planner need to be smoothed before generating actuation signals. Our proposed planner provides guidance signal that can be directly used to generate actuation signals. Also the proposed approach results in trajectories that are very smooth (infinitely differentiable), therefore, energy consumption due to motion is reduced. Simulations show the effectiveness of our approach. In future work, we intend to use real-time signal strength and obstacle information data in out approach.

Device-Free Target Following with Deep Spatial and Temporal Structures of CSI

A novel device-free target tracking and following method is proposed based on both the received signal strength (RSS) and channel state information (CSI) of WiFi signals. Different from the typical device-free target tracking method, we consider a scenario where the device-free target under tracking is followed by a device that transmits the reference signals for location tracking of the target and the device itself. To meet the goal, a deep spatial-temporal neural network model is designed to learn and exploit the multi-resolution spatial and temporal features of RSSI and CSI for location tracking. By experimental results on our testbed, we show that the average positioning accuracy of the proposed method for the device-free target can reach 0.773 meters, which has a 64% improvement over the accuracy of 2.164 meters of a typical device-free tracking method under the same experimental condition.

Joint Direct Estimation of Emitter Location and Antenna Beam Direction Based on Baseband Reconstruction

This paper addresses the problem of locating a stationary noncooperative emitter using multiple moving receivers. A signal baseband reconstruction based algorithm is proposed to locate the emitter directly from the received signals, without assuming static delay and Doppler shift in any observing interval. Besides the information of time-differences-of arrival (TDOA) and frequency-differences-of-arrival (FDOA), the received signal strength (RSS) information is further utilized when the source emits signals in free space with an omni antenna. In a more complex scenario where the emitter adopts a directional antenna, an iterative algorithm is proposed to jointly estimate the emitter location and its antenna beam direction, where a traditional direct position determination (DPD) method is used to initiate the iteration. The Cramér-Rao lower bounds (CRLBs) of the emitter position and the beam direction are derived. Three numerical cases are included to investigate the performance of the proposed algorithms. The signal baseband reconstruction based DPD algorithm shows improved applicability when the assumption of static time delay and Doppler shift is inappropriate and performs similarly with the classical DPD methods when the assumption holds. In case of free space propagation, it shows that the localization accuracy can be improved when RSS is further utilized. When the transmitting beam is directional, however, simply ignoring the directivity characteristics would lead to nonnegligible localization biases. By jointly estimating the emitter location and antenna beam direction, the RSS information can be effectively utilized to improve the localization accuracy, and a much more accurate estimation of beam direction can be obtained simultaneously.

Direct Position Determination Using Compressive Sensing Measurements Without Reconstruction

This paper proposes a direct position determination (DPD) method for stationary emitters with compressed signal measurements. Employing Hadamard matrix properties, the method estimates the source location directly in the compressing measurement domain, without the step of signal reconstruction and time-differences-of-arrival (TDOA) parameter extraction. In case of free space propagation, an augmented DPD method is proposed to further utilize the position related received signal strength (RSS) information. In addition, the Cram&#x00E9;r-Rao lower bounds (CRLBs) for emitter position of both DPD methods are derived for performance evaluation.

UHF Sensors Based Localization of Partial Discharge Sources in Air-Insulated Electrical Substation Using TDOA and Relative RSS Information

Time-difference of arrival (TDOA) based localization of partial discharge (PD) sources using Ultra High Frequency (UHF) technique often provides uncertain accuracy because of unavoidable errors in TDOA estimation between PD pulses. This paper presents a novel localization approach utilizing TDOA and relative received signal strength (RRSS) information together for improving the localization accuracy of PD sources within air-insulated substations (AIS). A complete PD localization system governed by this approach has been developed using multiple UHF PD sensors. The effectiveness of the proposed PD source localization approach has been validated through laboratory and field-based experiments in real-life AIS.

An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received Signal Strength information from available Wireless Local Area Network access points, together with the Wireless Sensor Networks technology. Six signals were recorded on a regular grid of anchor points, covering the research space. An optimization was performed by relative signal weighting, to minimize the average positioning error over the research space. The optimization process was conducted using a standard Quantum Particle Swarm Optimization, while the position error estimate for all given sets of weighted signals was performed using a Multilayer Perceptron (MLP) neural network. Compared to our previous research works, the MLP architecture was improved to three hidden layers and its learning parameters were finely tuned. These experimental results led to the 20% reduction of the positioning error when a suitable set of signal weights was calculated in the optimization process. Our final achieved value of 0.725 m of the location incertitude shows a sensible improvement compared to our previous results.

A novel hybrid range-free approach to locate sensor nodes in 3D WSN using GWO-FA algorithm

The precise node location of the sensor nodes is an essential requirement in wireless sensor networks (WSNs) to determine the place or event occurring at a particular instant of time. In WSN, existing localization schemes consider two-dimensional (2D) space, while in actual life, sensor nodes are placed in three-dimensional (3D) space. In 3D localization, there are many research challenges, such as higher computational complexity, poor location prediction, lesser coverage, and depending only on fewer anchor nodes. To address various research issues in a 3D environment we propose a range-free technique applied in an anisotropic scenario having degree of irregularity (DOI) as 0.01 using the concepts of a fuzzy logic system (FLS). Anisotropic properties of nodes are considered to determine the efficiency of Grey wolf with the Firefly algorithm. In our proposed scenario, the received signal strength (RSS) information is necessary among the target nodes and their corresponding anchor nodes for determining the location of target nodes using the information based on edge weights. These edge weights are further modeled using Hybrid Grey Wolf Optimization with Firefly Algorithm (GWO-FA) to estimate the location of target nodes. The proposed algorithm is energy efficient as a single location-aware node is used for localization. Further, the concept of virtual anchors is introduced that helps the algorithm to determine 3D positions.

Pedestrian Attitude Estimation and Recognition Algorithm Based on RF Data

Pedestrian detection plays a vital role in the estimation of human posture, especially the pedestrian detector, which provides the position information of human body in the image. This paper proposes a pedestrian gesture recognition algorithm using radio frequency data based on RFID technology. Its goal is to serve as a starting point for research in the field of human gesture recognition. The position of pedestrians in the input picture can be adaptively adjusted by learning the transformation parameters of samples during network training, and the local features of each block can be trained by multiple cross entropy functions at the network’s end, allowing the sample local area to be fully utilized as the network’s training. More accurate DFL and attitude feature recognition can be achieved using the measured received signal strength information and channel state information. The pedestrian gesture recognition algorithm based on RF data proposed in this paper improves pedestrian recognition accuracy, especially in noisy environments, such as fuzzy or occlusion, according to experimental results.

Multimodal Indoor Localization: Fusion Possibilities of Ultrasonic and Bluetooth Low-Energy Data

The resilience of indoor localization systems is a main concern of their industrial application. A combination of different techniques can enhance the overall robustness of such systems. In this work, we present fusion possibilities of coarse Bluetooth Low Energy localization based on the received signal strength indicator and the finer ultrasound time difference of arrival (TDOA) technique. This approach offers the advantage to robustify the high-accuracy ultrasonic localization in areas with non-optimal coverage. Moreover, the data fusion enables to enhance the overall localization area in a cost effective manner. This contribution proposes and evaluates (i) novel methods of how the ultrasonic system can be extended to a constrained area and (ii) a novel possibility to incorporate available Bluetooth signal strength information in the TDOA algorithm to improve accuracy.

Wi-Fi Direction Finding With Frequency-Scanned Antenna and Channel-Hopping Scheme

A novel and simple technique for Direction of Arrival (DoA) estimation in 802.11 Wi-Fi networks is proposed. It is based on the use of a dual-port frequency-beam scanning antenna, which is connected to single MIMO access point. By simply performing a channel hopping scheme and acquiring Received Signal Strength Information (RSSI) from each channel, the DoA can be estimated in a wide Field of View of 180° with a mean angular error of 5°. Due to its simplicity, a single commodity Wi-Fi AP can be used without the need of Channel State Information (CSI), or complex electronically reconfigurable or mechanically revolving antennas.

Improving Crowd Density Estimation by Fusing Aerial Images and Radio Signals

A recent line of research focuses on crowd density estimation from RGB images for a variety of applications, for example, surveillance and traffic flow control. The performance drops dramatically for low-quality images, such as occlusion, or poor light conditions. However, people are equipped with various wireless devices, allowing the received signals to be easily collected at the base station. As such, another line of research utilizes received signals for crowd counting. Nevertheless, received signals offer only information regarding the number of people, while an accurate density map cannot be derived. As unmanned aerial vehicles (UAVs) are now treated as flying base stations and equipped with cameras, we make the first attempt to leverage both RGB images and received signals for crowd density estimation on UAVs. Specifically, we propose a novel network to effectively fuse the RGB images and received signal strength (RSS) information. Moreover, we design a new loss function that considers the uncertainty from RSS and makes the prediction consistent with the received signals. Experimental results show that the proposed method successfully helps break the limit of traditional crowd density estimation methods and achieves state-of-the-art performance. The proposed dataset is released as a public download for future research.

Genetic Algorithm for Path Loss Model Selection in Signal Strength-Based Indoor Localization

Ranging methods using received signal strength (RSS) information are widely used for indoor localization because they can be easily implemented without conducting site surveys. However, range estimation with a single pathloss model produces considerable errors, which degrade the positioning performance. This problem mainly arises because the single pathloss model cannot reflect diverse indoor radio wave propagation characteristics. In this study, we develop a new overlapping multi-state model to consider multiple candidates of pathloss models including line-of-sight (LOS) and non-line-of-sight (NLOS) states, and propose an efficient way to select a proper model for each reference node involved in the localization process. To this end, we formulate a cost function whose value varies widely depending on the choice of pathloss model of each access point. Because the computational complexity to find an optimal channel model for each reference node exponentially increases with the number of reference nodes, we apply a genetic algorithm to significantly reduce the complexity so that the proposed method can be executed in real-time. Experimental validations with ray-tracing simulations and RSS measurements at a real site confirm the improvement of localization accuracy for Wi-Fi in indoor environments. The proposed method achieves up to 1.92 m mean positioning error under a practical indoor environment and produces a performance improvement of 31.09% over the benchmark scenario.

Enhanced Geometric Filtering Method Based Device-Free Localization With UWB Wireless Network

The measurement of received signal strength (RSS) and channel state information (CSI) has been widely used in the research of device-free localization (DFL). However, DFL systems based on these systems usually require labour-intensive training to understand the impact of the target on the surrounding wireless signals to build the target location-related model or image. In addition, the fact that the RSS is susceptible to multipath interference and is not directly accessible to CSI directly limits its actual deployment. In this paper, we explore the feasibility of DFL based on time-of-flight measurements of ultra-wideband signals, and propose a fusion method for simultaneous localization of multiple targets based on the evaluation results. Specifically, we mesh the feasible region of the target and utilize the geometric filtering method to re-evaluate the filtered wireless links in combination with the real-time adaptive relaxation target model. Consequently, we introduce the clustering method and combine the category matching strategy to ensure that the algorithm has robust real-time multi-target perception ability. Extensive experiments conducted in the lobby, laboratory and office scenarios demonstrate that the proposed method plays an active role in more than 82% and can control the mean error within 0.6 m.

Exploiting Fingerprint Correlation for Fingerprint-Based Indoor Localization: A Deep Learning Based Approach

For indoor localization system, the integration of various fingerprints could intuitively improve localization accuracy since different fingerprints complement each other. In spite of the potential benefits, however, the limitation of applying various fingerprints in improving localization accuracy still remains unknown. Moreover, how to design efficient indoor localization methods through fully exploiting the features of different fingerprints is to be explored as well. In this work, we investigate the location error of a fingerprint-based indoor system with the application of hybrid fingerprints. It manifests that the location error is dependent on correlation coefficient of different types of fingerprints. For instance, the exploiting correlation between different types of fingerprints is shown to effectively reduce the location error, which gradually decreases with the number of adopted fingerprints. On this basis, we propose a hybrid received signal strength (RSS) and channel state information (CSI) localization algorithm (HRC), which is designed based on deep learning. The HRC fully exploits quick construction of fingerprint database with coarse-grained RSS and rich multipath information of fine-grained CSI. The RSS and CSI with high correlation are selected to construct fingerprint database, aiming to improve localization accuracy. Moreover, a deep auto-encoder is used to reduce the computation complexity and the deep neural network is trained for location estimation. Experimental results, which are obtained by designed indoor localization system, validate that the location error of HRC can be reduced by 77.3% and 20.3%, compared with the existing localization methods and HRC without RSS/CSI selection by correlation coefficient, respectively.

Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

Sensible and secure IoT communication for digital twins, cyber twins, web twins

In order to effectively solve the current security problems encountered by smart wireless terminals in the digital twin biological network, to ensure the stable and efficient operation of the wireless communication network. This research aims to reduce the interference attack in the communication network, an interference source location scheme based on Mobile Tracker in the communication process of the Internet of Things (IoT) is designed. Firstly, this paper improves Attribute-Based Encryption (ABE) to meet the security and overhead requirements of digital twin networking communication. The access control policy is used to encrypt a random key, and the symmetric encryption scheme is used to hide the key. In addition, in the proposed interference source location technology, the influence of observation noise is reduced based on the principle of unscented Kalman filter, and the estimated interference source location is modified by the interference source motion model. In order to further evaluate the performance of the method proposed as the interference source, this paper simulates the jamming attack scenario. The Root Mean Square Error (RMSE) value of the proposed algorithm is 0.245 ​m, which is better than the ErrMin algorithm (0.313 ​m), and the number of observation nodes of the proposed algorithm is less than half of the ErrMin algorithm. To sum up, satisfactory results can be achieved by taking the Jamming Signal Strength (JSS) information as the observation value and estimating the location of the interference source and other state information based on the untracked Kalman filter algorithm. This research has significant value for the secure communication of the digital twins in the IoT.

Real‐time implementation of distributed beamforming for simultaneous wireless information and power transfer in interference channels

In this paper, we propose one-bit feedback-based distributed beamforming (DBF) techniques for simultaneous wireless information and power transfer in interference channels where the information transfer and power transfer networks coexist in the same frequency spectrum band. In a power transfer network, multiple distributed energy transmission nodes transmit their energy signals to a single energy receiving node capable of harvesting wireless radio frequency energy. Here, by considering the Internet-of-Things sensor network, the energy harvesting/information decoding receivers (ERx/IRx) can report their status (which may include the received signal strength, interference, and channel state information) through one-bit feedback channels. To maximize the amount of energy transferred to the ERx and simultaneously minimize the interference to the IRx, we developed a DBF technique based on one-bit feedback from the ERx/IRx without sharing the information among distributed transmit nodes. Finally, the proposed DBF algorithm in the interference channel is verified through the simulations and also implemented in real time by using GNU radio and universal software radio peripheral.

Localisation algorithm for security access control in railway communications

With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal strength (RSS) and channel state information (CSI) in railway communications are measured by AR9344 network interface card. Then, based on the measurement data, the authors propose trajectory-based, neural network-based (NN-based) and ray tracing-based (RT-based) localisation algorithms, serving for location-based security access control. Specifically, the trajectory-based algorithm combined with trajectory simulation, movement detection and dynamic time warping algorithms, realises passengers enter/exit pattern detection. The NN-based algorithm leverages back-propagation network (BPN) and constructs training sets with measurement-based RSS and CSI, finishing accurate localisation. Besides, they evaluate the algorithm performance under different layers of BPN. RT-based localisation algorithm combines measurement data and simulation analysis, leveraging simulated-based multiple-input multiple-output received power and delay spread to realise lightweight localisation. After evaluation, the RT-based algorithm can achieve the highest accuracy of localisation, up to 99.9% and is designed to be straightforward for integration with commercial access points and deployment to railway communications.

A Hybrid TDOA-Fingerprinting-Based Localization System for LTE Network

To the best of our knowledge, effective indoor positioning for the long term evolution (LTE) has not been addressed in the literature. In this paper, the uplink sounding reference signal (SRS), which contains timing and received signal strength (RSS) information, is exploited to achieve this task. Accordingly, an LTE based localization system using time-difference-of-arrival (TDOA) and fingerprint of RSS that operates in two steps is devised to meet the requirement of high-precision indoor positioning. In the first step, peak value detection is applied to the SRSs from spatially separated sensors to estimate the TDOAs, from which a coarse target location is computed with the use of least squares. Based on deep neural network, fingerprinting in a subarea containing the coarse solution is then performed to obtain the final position estimate. Due to the instability in the RSS values caused by non-line-of-sight and multipath propagation, we propose a novel extraction method to select reliable RSSs as input to the network. Experimental results show that our two-step localization system can provide an accuracy of sub-meter level.

CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

Wireless local area network (WLAN) fingerprint-based localization has become the most attractive and popular approach for indoor localization. However, the primary concern for its practical implementation is the laborious manual effort of calibrating sufficient location-labeled fingerprints. The Semi-supervised extreme learning machine (SELM) performs well in reducing calibration effort. Traditional SELM methods only use Received signal strength (RSS) information to construct the neighbor graph and ignores location information, which helps recognizing prior information for manifold alignments. We propose Composite SELM (CSELM) method by using both RSS signals and location information to construct composite graph. Besides, the issue of unlabeled RSS data quality has not been solved. We propose a novel approach called Composite semisupervised extreme learning machine with unlabeled RSS Quality estimation (CSELM-QE) that takes into account the quality of unlabeled RSS data and combines the composite neighbor graph, which considers location information in the semi-supervised extreme learning machine. Experimental results show that the CSELM-QE could construct a precise localization model, reduce the calibration effort for radio map construction and improve localization accuracy. Our quality estimation method can be applied to other methods that need to retain high quality unlabeled Received signal strength data to improve model accuracy.