RSSI-based Localization System Research Articles

Machine Learning-based Indoor Positioning Systems Using Multi-Channel Information

The received signal strength indicator (RSSI) is a metric of the power measured by a sensor in a receiver. Many indoor positioning technologies use RSSI to locate objects in indoor environments. Their positioning accuracy is significantly affected by reflection and absorption from walls, and by non-stationary objects such as doors and people. Therefore, it is necessary to increase transceivers in the environment to reduce positioning errors. This paper proposes an indoor positioning technology that uses the machine learning algorithm of channel state information (CSI) combined with fingerprinting. The experimental results showed that the proposed method outperformed traditional RSSI-based localization systems in terms of average positioning accuracy up to 6.13% and 54.79% for random forest (RF) and back propagation neural networks (BPNN), respectively.

Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression

The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.

3-D Indoor Localization and Identification Through RSSI-Based Angle of Arrival Estimation With Real Wi-Fi Signals

A highly accurate 3-D indoor passive localization and identification system is presented in this article. The system can monitor various commercial Wi-Fi devices through the proposed received signal strength indicator (RSSI)-based angle of arrival (AoA) estimation technique. RSSI-based localization systems conventionally use additional assistance techniques, such as distance–RSSI calibration, fingerprint analysis, machine learning, and a widespread setup to achieve high accuracy. On the contrary, this proposed system can operate in complex environments full of scattering objects and obstructions without requiring any additional assistance techniques. The proposed technique uses a six-port network to evaluate the phase difference in the carrier waves of Wi-Fi signals without the influence of modulated signals. The network also preserves the modulated signals without the influence of the phase difference of the carrier waves such that Wi-Fi devices can be identified. Regarding practical applications, the system is designed to be capable of detecting devices through walls and thus can be hidden outside the monitored room. The experimental results indicate that in single-source localization, the average error is 0.089 m; in multiple-source localization, it is 0.354 m; and for seeing through the wall, it is 0.24 m. In the worst case scenario, the error is still smaller than 0.63 m. The accuracy in all the experimental results was found to be at the decimeter level. In summary, this study experimentally validated a 3-D indoor localization and identification system for diverse Wi-Fi devices in various environments.

An Adaptive Energy Saving Algorithm for an RSSI-Based Localization System in Mobile Radio Sensors.

In localization systems based on the emission of reference radio signals, an important issue related to the reliability of sensor operation is the problem of operating time and power of the emitted reference radio signal. There are many localization methods that have proven useful in practice and that use a reference radio signal for this purpose. In the issue of determining the location of radio emitters, various radio signal propagation models are used to determine the effective range and distance of the sensor-receiver from the radio emitter. This paper presents an adaptive power control algorithm for a transmitter, as a reference emitter, operating in power-saving mode. An important advantage of the presented solution is the adjustment of the localization system accuracy at the assumed level of energy radiated by radio emitters based on the RSSI signal received power estimation.

A Low-Cost RSSI-Based Localization System for Wildlife Tracking

This paper presents a low-cost localization system for the high-resolution tracking of bats. The system bases on a ground network consisting of multiple low-cost receiver stations, and ultra-lightweight transmitters mounted on the bats. A main challenge of the received signal strength based localization is the limited dynamic range of the employed low-cost receivers. This challenge is solved using an efficient 2-stage differential correlation concept. It significantly improves the dynamic wrt. conventional power detection methods. In addition, this concept requires low processing power and is robust wrt. frequency offsets. Finally, this paper presents a performance evaluation employing reference measurements recorded in the rain forest of Panama.

Research on RSSI–Based Localization System in the Wireless Sensor Network

Due to the rapid development in wireless sensor networks, the applications of the WSN have been extended to different environment and achieved a more diversified purpose. AS for the localization system, there are many related re- search achievements. A set of localization system is built through the system integration between the small-scale wireless hardware sensor and the wireless sensor platform, through the use of the RSSI and the integration of the fuzzy inference system. The blind nodes are experimented in the indoor environment so that the system's efficiency can be verified. The system is implemented in the 8 meters multiplying 8 meters indoor space which is divided into 16 sections. Then 4 anchor nodes are placed in the corners and the fuzzy identification system is built for the nodes' RSSI signal in the blocks. Later, the localization test is implemented in the random positions within the blocks. The experimental results show that the lo- calization system accuracy rate is more than 80% and can successfully reach the preliminary localization system.