Implementation and test of an RSSI-based indoor target localization system: Human movement effects on the accuracy (original) (raw)
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Sensing and Imaging, 2020
In a received signal strength indicator (RSSI) based localization system, the presence or movement of humans is one of the major effects causing RSSI variation. Using RSSI data during such a situation to estimate the target position can give large errors. Regarding this problem, in this paper, a comparison of several RSSI-based localization methods with and without human presence and movement were investigated experimentally. The major contribution of this work is that the well-known and widely used RSSI-based localization methods presented in the literature, including the min-max, the trilateration, the weighted centroid localization (WCL), and the relative span exponential weighted localization (REWL) methods, were tested. Thus, how human presence or absence influences the accuracy of these methods, and which methods show the best estimates while tolerating human movement effects can be investigated. The experiments were carried out in a laboratory and in a parking building. The results demonstrate that, without human movement effects, all methods perform very similarly. In contrast, human movements significantly increased estimation errors.Here, the maximum distance errors of the min-max, the trilateration, the WCL, and the REWL are 1.34 m, 4.09 m, 1.25 m, and 1.24 m, respectively. Obviously, the min-max, the WCL (with an optimal parameter), and the REWL (with the optimal parameter) can well tolerate the RSSI variations caused by human movements and provide significantly better accuracy than the trilateration method. Based on these findings, all the mentioned localization methods should be further improved to deal with the human movement problem.
Performances of an RSSI-based positioning and tracking algorithm
2011 International Conference on Indoor Positioning and Indoor Navigation, 2011
This paper reports the results of a positioning and tracking algorithm for indoor environments based on simulated and pre-computed attenuation map values. The localization is performed through a global optimization that minimizes a cost function computed in the data-space, which is the attenuation reference map relative to the environment under test. The tracking is implemented introducing a correlation between the current position and the previous ones. Two environments of different size, shape and characteristics are chosen for the algorithm validation.
International Journal on Smart Sensing and Intelligent Systems
Existing researches on location tracking focus either entirely on indoor or entirely on outdoor by using different devices and techniques. Several solutions have been proposed to adopt a single location sensing technology that fits in both situations. This paper aims to track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error. RSSI (Received Signal Strength Indication) technique together with enhancement algorithms is proposed to cater this solution. The proposed RSSI-based tracking technique is divided into two main phases, namely the calibration of RSSI coefficients (deterministic phase) and the distance along with position estimation of user location by iterative trilateration (probabilistic phase). A low complexity RSSI smoothing algorithm is implemented to minimize the dynamic fluctuation of radio signal received from each reference node when the target node is moving. Experiment measurements are carried out to analyze the sensitivity of RSSI. The results reveal the feasibility of these algorithms in designing a more accurate real-time position monitoring system.
Filtering Effect on RSSI-Based Indoor Localization Methods
Tanzania Journal of Engeering and Technology, 2022
Indoor positioning systems are used to locate and track objects in an indoor environment. Distance estimation is done using received signal strength indicator (RSSI) of radio frequency signals. However, RSSI is prone to noise and interference which can greatly affect the accuracy performance of the system. In this paper Internet of Things (IoT) technologies like low energy Bluetooth (BLE), WiFi, LoRaWAN and ZigBee are used to obtain indoor positioning. Adopting the existing trilateration and positioning algorithms, the Kalman, Fast Fourier Transform (FFT) and Particle filtering methods are employed to denoise the received RSSI signals to improve positioning accuracy. Experimental results show that choice of filtering method is of significance in improving the positioning accuracy. While FFT and Particle methods had no significant effect on the positioning accuracy, Kalman filter has proved to be the method of choice for BLE, WiFi, LoRaWAN and ZigBee. Compared with unfiltered RSSI, results showed that accuracy was improved by 2% in BLE, 3% in WiFi, 22% in LoRaWAN and 17% in ZigBee technology for Kalman filtering method. ARTICLE INFO
An RSSI Classification and Tracing Algorithm to Improve Trilateration-Based Positioning
Sensors, 2020
Received signal strength indicator (RSSI)-based positioning is suitable for large-scale applications due to its advantages of low cost and high accuracy. However, it suffers from low stability because RSSI is easily blocked and easily interfered with by objects and environmental effects. Therefore, this paper proposed a tri-partition RSSI classification and its tracing algorithm as an RSSI filter. The proposed filter shows an available feature, where small test RSSI samples gain a low deviation of less than 1 dBm from a large RSSI sample collected about 10 min, and the sub-classification RSSIs conform to normal distribution when the minimum sample count is greater than 20. The proposed filter also offers several advantages compared to the mean filter, including lower variance range with an overall range of around 1 dBm, 25.9% decreased sample variance, and 65% probability of mitigating RSSI left-skewness. We experimentally confirmed the proposed filter worked in the path-loss expone...
Journal of Electrical Engineering & Technology, 2020
In this paper, an extended weighted centroid localization (EWCL) method for a received signal strength indicator (RSSI)based localization system is proposed. In the traditional WCL method, an unknown target position is estimated using the actual position of each reference node and RSSI information collected from each reference node based on the distance between the target and each reference node. The estimation accuracy is significantly increased when more reference nodes are applied in the localization system. In addition, in the traditional WCL method, four reference nodes deployed in a square area are initialized and tested. Such a configuration produces good localization results. By this information, in this work, the EWCL method is developed to estimate a target's position taking into consideration information from four reference nodes, where only three actual reference nodes are deployed in a real test area. Here, the fourth reference node position as the virtual reference node position is defined, and a new technique involving a guideline solution is developed to estimate the distance between the target and the fourth reference node using distance information from the three actual reference nodes. By our purpose, the hardware cost and the complexity of the experimental setup can be reduced, while the estimation accuracy can be increased. To verify the EWCL method, both theoretical and experimental studies are performed. The theoretical study first demonstrates the EWCL performance without the effect of radio signals. The experimental study using low-cost lowpower 2.4 GHz nodes deployed in a semi-outdoor test area reveals more realistic results. The results indicate that the EWCL method significantly outperforms the WCL method in all test cases. The localization error is reduced by 49.151% in the theoretical test. In the experimental test, the localization errors are reduced by 33.708 and 28.351% for scenarios without and with a human presence, respectively.
Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks
Proceedings of the workshop …, 2008
In this paper, we investigate the actual performance of some of the best known localization algorithms when deployed in realworld indoor environments. Among the plethora of possible localization schemes, we focus on those based on radio signal strength measurements only, since they do not require extra circuitry that would result in higher cost and energy consumption. For a fair comparison, we have first gathered thousands of radio signal strength measurements in two different indoor environments. To estimate the channel model parameters and to compare the different localization algorithms these data have been used.
Rssi Based Location Estimation in a Wi-Fi Environment: An Experimental Study
ICTACT Journal on Communication Technology, 2014
In real life situations, location estimation of moving objects, armed personnel are of great importance. In this paper, we have attempted to locate targets which are mobile in a Wi-Fi environment. Radio Frequency (RF) localization techniques based on Received Signal Strength Indication (RSSI) algorithms are used. This study utilises Wireless Mon tool, software to provide complete technical information regarding received signal strength obtained from different wireless access points available in a campus Wi-Fi environment, considered for the study. All simulations have been done in MATLAB. The target location estimated by this approach agrees well with the actual GPS data.