RSSI-based Distance Estimation Research Articles

ЗАСТОСУВАННЯ ФІЛЬТРА ЧАСТОЧОК ДО ЗАДАЧ САМОЛОКАЛІЗАЦІЇ ЛИШЕ ЗА ВІДСТАННЮ ДО ДЖЕРЕЛ РАДІОСИГНАЛІВ

This work is aimed at searching for a relatively accurate and computationally efficient algorithm for range-only self-localization. Key requirement to the data sources is cost effectiveness. The radio signal strength indicator (RSSI)-based distance estimation model was chosen, due to wide availability of hardware and cost effectiveness. Key requirements to the algorithm are robustness towards noise introduced by RSSI-based distance estimation errors and flexibility to introduce additional data sources. Particle filter algorithm was chosen, because it satisfies both requirements, although it has bigger computational costs comparing to Kalman filter. A simulation environment was created to conduct experiments with assumed transmitted signal strength of 1W and square root signal decay law. A Particle filter-based actor was implemented. Actor starts by translating geo-spatial coordinates of beacons into relative planar cartesian coordinates, generates particles around beacons. Particles are placed with uniform distribution within a torus with co-planar circular axis radius equal to distance estimation. In each experiment, simulation software walks actor through a pre-defined path, compares obtained position estimates with expected state and calculates MAE and RMSE. Obtained results were satisfactory, in some cases showing MAE=2.5981 and RMSE=2.8474 after short stabilization period of few iterations. This period exists due to suboptimal initial particles placement. A Kalman filter-based actor was implemented as a comparison. A reference Kalman filter-based implementation showed slightly worse overall results with MAE=12.5199 and RMSE=13.2238. This can be explained by a lack of input from IMU. Further research will be directed towards UAV swarm self-localization and collision avoidance.

UAM-RDE: an uncertainty analysis method for RSSI-based distance estimation in wireless sensor networks

Distance estimation between sensor nodes is crucial to localization and object tracking in a wireless sensor network. The received signal strength indicator is widely used for wireless distance estimation, because of its advantages, including availability, low cost, flexibility, and so on. As is well known, RSSI measurement values are extremely susceptible to the surroundings, resulting in uncertain distance estimations. Without confidential information, the distance estimation results could not provide valuable priori information for follow-up processing methods and applications, such as localization, navigation, and guidance. In this paper, we propose a new uncertainty analysis method for RSSI-based distance estimation (UAM-RDE) to study the uncertainty propagation in RSSI-based distance estimations. In UAM-RDE, we explore the uncertainty propagation mechanism from the input to the output of the RSSI-based distance estimation, including uncertainty factor analysis, sensitivity analysis, propagation, and synthesis of uncertainty. The simulations and experimental results validate and demonstrate the feasibility of UAM-RDE.

Accuracy Enhancement of RSSI-based Distance Estimation by Applying Gaussian Filter

RSSI values of Bluetooth Low Energy (BLE) beacon are unreliable to use localization. To cope with this problem, we propose a new localization algorithm that enhances the accuracy of RSSI value. The proposed algorithm applies Gaussian filter to RSSI values from BLE Beacons, and then uses weight value based on filtered RSSI quality. Background/Objectives: Friis formula is used to calculate distance between a BLE beacon and BLE scanners using triangulation scheme. Finally, Gaussian filtering is applied twice to the location values for accuracy improvement. Findings: Experiments are performed in indoor environment, and experiment data is calculated by MATLAB to make graph and chart for easily comparing location result. Experiment result shows that DGF algorithm shows more accurate and reliable localization result than commonly used Kalman filter algorithm. Furthermore, DGF algorithm is very effective to calculate not only distance but also location. Improvements: DGF algorithm indicates excellent performance when we adapt weight value. The proposed system can be used to tracking the location of BLE beacon in real-time.

An empirical investigation of RSSI-based distance estimation for wireless indoor positioning system

RSSI-based distance estimation techniques for wireless indoor positioning system require extensive offline calibration to construct propagation model in order to describe the relationship between received signal strength and distance. This paper investigates the accuracy of the well-known propagation models against the measured data at indoor building. From the results, the dual slope model exhibits the best propagation model and it is chosen as the reference for further investigation. The accuracy of dual slope model in distance estimation suffers from the degradation due to the presence of Non Line of Sight NLOS condition between mobile station and access point. Therefore, to further improve the accuracy, this paper studies the effect of breakpoint distance and evaluates two simple techniques, running variance and kurtosis index, to identify the NLOS condition. Once the NLOS condition is identified, the best dual slope model can be selected for accurate distance estimation.

An architecture for secure positioning in a UAV swarm using RSSI-based distance estimation

The widespread of small unmanned aerial vehicles (UAVs) offers more opportunities for collaborative UAV swarm to optimise missions. The common application for UAV swarms are surveillance, path planning, airborne, and relay networks. Cooperative applications make use of the UAVs' locations to make decisions. However, the question of vulnerability to security must be considered when the system infers that there are benefits and rights based on the UAV's location. There is a risk that an attacker can cheat the system by claiming a false or inaccurate location to gain access to restricted resources or be engaged in malicious activities without detection. This makes the system be very sensitive and dependent on the trust of the node's geo-temporal information in a swarm. In this paper, we propose an architecture that uses a UAV wireless card to measure the distance between a UAV, called a prover node , and the set of verifier nodes closest to it. The architecture core is based on a multilateration algorithm, which is employed to estimate the prover's position. In a previous study, we applied image processing from a UAV payload stereo camera to measure the distances between the nodes. In this paper, we have extended the study to securing positioning in a UAV swarm by applying the fundamental principles of beaconing-based communication to calculate the distance between the nodes on the basis of the received signal strength indicator (RSSI). The simulation results showed that the correct position determined by RSSI was better than the image processing from stereo cameras. The degree of accuracy achieved from RSSI was greater than 99.6% in distances of up to 165 m , while a similar degree of accuracy was limited up to 100 m of the distance, when a high-definition stereo camera was used.

Uncertain data clustering-based distance estimation in Wireless Sensor Networks.

For communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncertainties in RSSI readings due to obstacles, wireless interferences, etc. In this paper, we specifically propose a novel RSSI-based communication distance estimation method based on the idea of interval data clustering. We first use interval data, combined with statistical information of RSSI values, to interpret the distribution characteristics of RSSI. We then use interval data hard clustering and soft clustering to overcome different levels of RSSI uncertainties, respectively. We have used real RSSI measurements to evaluate our communication distance estimation method in three representative wireless environments. Extensive experimental results show that our communication distance estimation method can effectively achieve promising estimation accuracy with high efficiency when compared to other state-of-art approaches.