Received Signal Strength Information Research Articles

Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results.

Hybrid Positioning Algorithm for Tilted Receiver Using RSS and TDOA with Gaussian Process

In the visible light positioning (VLP) system, the received signal strength (RSS) algorithm has a better signal noise ratio performance than the time difference of arrival (TDOA) algorithm, while the RSS algorithm needs to work under the condition that the transmitter and receiver are strictly parallel. However, the receiver is prone to tilt due to environmental disturbances, which reduces the accuracy of the RSS algorithm. For the tilted receiver, the TDOA algorithm has a higher positioning accuracy than the RSS algorithm. In order to take full advantage of the two algorithms, we propose a hybrid positioning algorithm to locate the tilted receiver by using a Gaussian process (GP). The scheme separately uses RSS and the distance difference as the inputs of the GP model to estimate the position of the receiver. Then, according to the proposed positioning selection strategy, the more credible estimated position in our opinion is selected as the final estimated position. In addition, RSS information in the hybrid algorithm is extracted from the TDOA signal, which allows the hybrid algorithm to prevent an increase in the complexity of the VLP system. During the training and testing, RSS is normalized to meet the order-of-magnitude requirements of the GP model on the input data. Simulation results validate the hybrid algorithm based on a two-dimensional positioning system for the tilted receiver. When the standard deviations of the azimuth angle and elevation angle are 1°, the positioning accuracy of the hybrid algorithm is 53.7% higher than that of the RSS algorithm using an artificial neural network, and 49.9% higher than that of the RSS algorithm using a GP. The localization error under 1° standard deviations of azimuth and elevation angles is 20.2% lower than that under 20° standard deviations of the two angles.

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.

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.

Skeleton-Based Positioning and Reference Point Deployment for Hybrid Wireless Localization

Nowadays, Wi-Fi or Bluetooth based indoor localization algorithms are designed to improve the effects from variation of receive signal strength (RSS) at different locations. However, the positioning accuracy of targeting device is highly related to the indoor topological layout, e.g., the blocking effects from partitioning walls. In this paper we propose a novel skeleton-based positioning and reference point deployment for hybrid wireless localization (S-PDH) that utilizes the spatial skeleton of indoor layout to improve location estimation accuracy. Firstly, we adopt an automatic map analysis algorithm to generate appropriate reference points (RPs) from the spatial skeleton of indoor layout. Secondly, we propose a novel clustering algorithm to consider both spatial layout and RSS information in order to enhance the accuracy of RP matching. Those outlier RPs resulting poor performance will be replaced by appropriate deployment of BLE APs. Finally, the cluster-based positioning algorithm is presented to estimate the target’s location. Compared to existing methods, both the simulation and experimental results show that the proposed S-PDH system can achieve better positioning accuracy along with the benefits of automatically generating the spatial layout information for wireless indoor localization.

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.

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.

6G Multisource-Information-Fusion-Based Indoor Positioning via Gaussian Kernel Density Estimation

With the in-depth development of the Internet of Things (IoT) and the constant discussion of 6G visions, the future development of indoor location-based services (LBSs) in the 6G-enabled IoT is attracting people’s attention. In fact, due to the continuous updating of network techniques, the complexity of the indoor environment and the number of connected wireless access points (APs) will increase dramatically, which leads to the diversity of the received signal strength (RSS) on the performance of signal propagation distance estimation, resulting in low positioning accuracy and poor robustness. In response to this problem, this article proposes a multisource information fusion-based indoor positioning approach via Gaussian kernel density estimation. First, the heuristic distribution model is used to establish the mathematical relationship between the RSS from different Wi-Fi APs and the signal propagation distance, and then the Gaussian kernel density estimation approach is applied to estimate the signal propagation distance distribution. Second, the normalized signal propagation distance distribution is set as the basic probability assignment in the Dempster–Shafer (D–S) evidence theory, and then the multisource RSS information is fused according to D–S evidence synthesis rules. Meanwhile, the trust function synthesized by the D–S evidence theory is used to select matching reference points (RPs). Finally, the fuzzy decision algorithm is used to select the ideal matching RPs from the matching RPs for the positioning. Experimental results show that the proposed approach has higher positioning accuracy and stronger positioning robustness compared to existing ones.

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.

Indoor optical wireless communication system with continuous and simultaneous positioning.

The optical wireless communication (OWC) technology has been widely studied to provide high-speed communications in indoor environments. The indoor OWC-based positioning function is also highly demanded and the received signal strength (RSS) method has attracted intensive interests, where multiple transmitters are used and the positioning information is provided by estimating the channel gain from each transmitter with known location. However, this process normally requires dedicated positioning time slots, RF carriers or codewords, which limit the system data rate and throughput. To solve this limitation, in this paper we propose a filter-enhanced indoor OWC positioning scheme, where spatial waveform shaping filters are applied to the transmitters, thus enabling the signals from different transmitters to be separated via matched filters at the receiver side. Hence, this approach allows the RSS information to be obtained from the wireless communication signal directly and the positioning function realized without affecting the wireless communication data rate or throughput. In addition, since positioning is realized using the communication signal, continuous positioning can be achieved for real-time tracking. The proposed filter-enhanced positioning scheme is experimentally demonstrated in a near-infrared indoor OWC system with laser transmitters. Results show that an average positioning accuracy of 5.41 cm and 2.5 Gb/s wireless communication are achieved simultaneously. The proposed filter-enhanced positioning scheme can also be applied in visible light communication (VLC) systems with LED transmitters, and the feasibility is verified via simulations. The proposed filter-enhanced scheme provides a promising positioning method in indoor OWC systems without affecting the wireless data communication.

Beyond RSS: A PRR and SNR Aided Localization System for Transceiver-free Target in Sparse Wireless Networks

Nowadays transceiver-free (also referred to as device-free) localization using Received Signal Strength (RSS) is a hot topic for researchers due to its widespread applicability. However, RSS is easily affected by the indoor environment, resulting in a dense deployment of reference nodes. Some hybrid systems have already been proposed to help RSS localization, but most of them require additional hardware support. In order to solve this problem, in this paper, we propose two algorithms, which leverage the Packet Received Rate (PRR) to help RSS localization without additional hardware support. Moreover, we take the environment noise information into consideration by utilizing the Signal-to-Noise Ratio (SNR) which is based on the RSS and Noise Floor (NF) information instead of pure RSS. Thus, we can alleviate the noise effect in the environment and make our system more sensitive to the target. Specifically, when reference nodes are sparsely deployed and RSS is very weak, PRR and SNR can help in performing localization more accurately. Our BEYOND RSS system is based on sparse wireless sensor networks, wherein the experimental results show that the average localization error of our approach outperforms the pure RSS based approach by about 15.19%.

RSS and Phase Kalman Filter Fusion for Improved Velocity Estimation in the Presence of Real-World Factors

Motion estimation of RFID tags has been shown to be possible using phase information, while position estimation is possible using received signal strength (RSS). This article combines phase and RSS information using a Kalman filter as sensor fusion technique. It compares the combined RSS and phase fusion Kalman filter technique for motion estimation with the phase only Kalman filter, exponential smoothing filter and raw phase motion estimation excluding a Kalman filter. The influence of frequency hopping broadcasting regulations on the performance of these techniques is investigated, an aspect that has not been properly addressed before. The primary factors found in real-world scenarios that have an effect on motion estimation techniques are identified as multipath reflections and the presence of multiple tags. The influence of such non-ideal environments are characterized and specific vulnerabilities of these techniques are investigated. Finally, the shortcoming of these techniques as well as advantages and possible environments where this technique can be successfully used are discussed.

Joint Channel Allocation and Power Control Based on Long Short-Term Memory Deep Q Network in Cognitive Radio Networks

Efficient spectrum resource management in cognitive radio networks (CRNs) is a promising method that improves the utilization of spectrum resource. In particular, the power control and channel allocation are of top priorities in spectrum resource management. Nevertheless, the joint design of power control and channel allocation is an NP-hard problem and the research is still in the preliminary stage. In this paper, we propose a novel joint approach based on long short-term memory deep Q network (LSTM-DQN). Our objective is to obtain the channel allocation schemes of the access points (APs) and the power control strategies of the secondary users (SUs). Specifically, the received signal strength information (RSSI) collected by the microbase stations is used as the input of LSTM-DQN. In this way, the collection of RSSI can be shared between users. After the training is completed, the APs are capable of selecting channels with small interference while the SUs may access the authorized channels in an underlay operation mode without knowing any knowledge about the primary users (PUs). Experimental results show that the channels are allocated to the APs with a lower probability of collision. Moreover, the SUs can adjust their power control strategies quickly to avoid the harmful interference to the PUs when the environment parameters change randomly. Consequently, the overall performance of CRNs and the utilization of spectrum resources are improved significantly compared to existing popular solutions.

An Efficient Fingerprint Database Construction Approach Based on Matrix Completion for Indoor Localization

The Received Signal Strength (RSS) fingerprint-based indoor localization method in Wireless Local Area Network (WLAN) environment has been widely used due to its low hardware requirements and high positioning accuracy. However, the positioning system requires enormous effort to collect fingerprint at each reference point in positioning area, which is time-consuming and laborious. For the practicability of the method, it is essential to reduce the workload of fingerprint collection in the offline phase and construct a fingerprint database efficiently and accurately. Utilizing the high spatial correlation of RSS, in this paper, an efficient fingerprint database construction method is proposed based on low-rank matrix completion. In the meanwhile, different from the traditional matrix completion model, we propose an improved optimization model by converting to the weighted nuclear norm and introducing l 1 -norm and Frobenius norm to recover unknown data accurately and reduce the influence of outliers and Gaussian noise simultaneously. Furthermore, we combine the K-Nearest Neighbor (KNN) method to solve the problem that matrix completion cannot deal with all unknown rows or columns in the matrix. The experiment results indicate that the proposed algorithm can recover the fingerprint database completely through a few RSS information and has higher accuracy than other algorithms in various scenarios.

Outage Bridging and Trajectory Recovery in Visible Light Positioning Using Insufficient RSS Information

Indoor positioning technology is vital for various location-aware applications while visible light positioning (VLP) is especially promising due to its ubiquitous and energy-efficient features. VLP has been widely investigated under the assumption of line of sight (LoS), yet, VLP signal blockage can happen frequently in a practical indoor environment and brings about outage problems to indoor localization/tracking services. However, this problem is usually overlooked or sidestepped in the existing works. Our work, for the first time, investigates the outage problem in a received signal strength (RSS)-based VLP system. Efficient algorithms for outage bridging and trajectory recovery are proposed by smartly fusing with insufficient RSS information. Specifically, a partial-RSS-assisted inertial navigation system (PRAINS) inspired by extended Kalman filter (EKF) is developed to bridge sporadic outage, while a bi-directional structured PRAINS (Bid-PRAINS) is developed to use both pre- and post- outage information to recover the lost trajectory information. To further deal with a more general situation when the system noise features are not pre-known and hard to be measured/estimated, a semi-parameterized RNN based learnable Kalman filter (SPR-LKF) is proposed in place of the EKF to learn the observation/transition noise features and optimize the estimation simultaneously through a recurrent neural network (RNN). Extensive tests show that the PRAINS/ Bid-PRAINS has at least 62% accuracy improvement over the conventional inertial navigation system (INS)-only algorithm, while the proposed SPR-LKF/ Bid-SPR-LKF can offer an even better accuracy gain of 70% even without pre-knowing the system noise feature.

Indoor Positioning Algorithm Fusing Multi-Source Information

With the development of computer technology, mobile intelligent terminal and wireless local area network (WLAN), the applications of location services have shown significant growth, and much progress has been made both in the applications and researches. According to the actual application requirements, a robust indoor positioning algorithm fusing multi-source information was presented in this paper. Firstly, the methods based on the inertial navigation system (INS) and the received signal strength (RSS) of WLAN were discussed and together with their advantages and disadvantages. Then, in order to further improve the positioning performance, a fusion model based on the sparse signal representation theory was designed to integrate the INS and RSS information, and next the optimization solution approach for the fusion model was deeply discussed. Finally, the simulation experiments were designed and carried out, and the experimental results demonstrated the feasibility and effectiveness of the proposed fusion algorithm.

A Grid Cumulative Probability Localization-Based Industrial Risk Monitoring System

With the rapid development of modern industries, more and more risk factors exist in industrial operations, leading to a gradual increase in the frequency of industrial failures. The industrial enterprises have taken a variety of measures to analyze the risk factors in industrial operations, but establishing an effective monitoring and evaluation system remains a technical challenge. To overcome this challenge, a practical industrial risk monitoring system, based on the wireless sensor network (WSN), is established in this paper to improve resilience of the respective industrial operations. In the proposed system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically as the anchor nodes of the localization system, and two grid cumulative probability localization (GCPL) algorithms are proposed based on the GCPL-circle intersection and GCPL-path loss methods, respectively. The GCPL algorithm determines the grid cumulative probability using prior location information of the target node and the received signal strength information between the anchor nodes and target node to locate the target node. Experimental results show that the two GCPL algorithms significantly improve the localization accuracy and stability, and hence can be implemented as a risk monitoring system for real-world applications. Note to Practitioners —In industrial operations, a lot of risk factors exist which result in many frequent operational disruptions and failures. It is hence critical to develop an effective risk monitoring and evaluation system. Motivated by the industrial needs, we develop in this paper a practical wireless sensor network (WSN)-based industrial risk monitoring system. In the system, the existing sensor nodes in the industrial environment are organized into WSN to transmit real-time data. A special inspection robot is used to monitor comprehensively the environmental and safety risk factors in the industrial environment. To obtain the location of the robot, the partial nodes of the WSN are organized dynamically. We further propose two grid cumulative probability localization algorithms to establish the system. We show via computational experiments that the proposed risk monitoring system performs well and can be implemented as a real application which helps to improve risk management of industrial operations.

Effective Fingerprint Extraction and Positioning Method Based on Crowdsourcing

At present, WiFi fingerprinting indoor positioning technology is a research hotspot, and the construction of a radio map based on crowdsourcing can significantly reduce the amount of labor required. However, when users collect fingerprint information using crowdsourcing data in practical applications, the received signal strength (RSS) information collected by user phones updates slowly and will remain the same for a certain distance and time. Therefore, crowdsourcing data is inaccurate, and a radio map thereby established will also lead to inaccurate indoor positioning. In order to address this issue, this paper proposes a method for extracting effective RSS information from crowdsourcing data in order to establish the radio map and achieve positioning. By analyzing the crowdsourcing data characteristics, effective RSS information is identified and extracted. Based on inertial sensors data, the position of effective RSS information is determined. The effective crowdsourcing data associated with the position is weighted and fused in order to establish the radio map. Aimed at the issue of the RSS collected by phones updating slowly, a combined positioning method for position calculation is proposed in order to achieve effective positioning. Experiments were conducted in a real environment to validate the algorithm, and the positioning accuracy could reach 1.5 m by using crowdsourcing data to construct the radio map and executing an indoor positioning algorithm, which is close to the accuracy achieved when using manual data collected by professionals at a certain distance and saves a great deal of labor.

Estimating the Physical Distance between Two Locations with Wi-Fi Received Signal Strength Information Using Obstacle-aware Approach

This study presents a new method for estimating the physical distance between two locations using Wi-Fi signals from APs observed by Wi-Fi signal receivers such as smartphones. We assume that a Wi-Fi signal strength vector is observed at location A and another Wi-Fi signal strength vector is observed at location B. With these two Wi-Fi signal strength vectors, we attempt to estimate the physical distance between locations A and B. In this study, we estimate the physical distance based on supervised machine learning and do not use labeled training data collected in an environment of interest. Note that, because signal propagation is greatly affected by obstacles such as walls, precisely estimating the distance between locations A and B is difficult when there is a wall between locations A and B. Our method first estimates whether or not there is a wall between locations A and B focusing on differences in signal propagation properties between 2.4 GHz and 5 GHz signals, and then estimates the physical distance using a neural network depending on the presence of walls. Because our approach is based on Wi-Fi signal strengths and does not require a site survey in an environment of interest, we believe that various context-aware applications can be easily implemented based on the distance estimation technique such as low-cost indoor navigation, the analysis and discovery of communities and groups, and Wi-Fi geo-fencing. Our experiment revealed that the proposed method achieved an MAE of about 3-4 meters and the performance is almost identical to an environment-dependent method, which is trained on labeled data collected in the same environment.

PRAPD: A novel received signal strength–based approach for practical rogue access point detection

Rogue access point attack is one of the most important security threats for wireless local networks and has attracted great attention from both academia and industry. Utilizing received signal strength information is an effective solution to detect rogue access points. However, the received signal strength information is formed by multi-dimensional received signal strength vectors that are collected by multiple sniffers, and these received signal strength vectors are inevitably lacking in some dimensions due to the limited wireless transmission range and link instability. This will result in high false alarm rate for rogue access point detection. To solve this issue, we propose a received signal strength–based practical rogue access point detection approach, considering missing received signal strength values in received signal strength vectors collected in practical environment. First, we present a preprocessing scheme for received signal strength vectors, eliminating missing values by means of data filling, filtering, and averaging. Then, we perform clustering analysis on the received signal strength vectors, where we design a distance measurement method that dynamically uses partial components in received signal strength vectors to minimize the distance deviation due to missing values. Finally, we conduct the experiments to evaluate the performance of the practical rogue access point detection. The results demonstrate that the practical rogue access point detection can significantly reduce the false alarm rate while ensuring a high detection rate.