Localization of static target in WSNs with least-squares and extended Kalman filter

Abstract

Wireless sensor network localization is an essential problem that has attracted increasing attention due to wide requirements such as in-door navigation, autonomous vehicle, intrusion detection, and so on. With the a priori knowledge of the positions of sensor nodes and their measurements to targets in the wireless sensor networks (WSNs), i.e. posterior knowledge, such as distance and angle measurements, it is possible to estimate the position of targets through different algorithms. In this contribution, two approaches based on least-squares and Kalman filter are described for localization of one static target in the WSNs with distance, angle, or both distance and angle measurements, respectively. Noting that the measurements of these sensors are generally noisy of certain degree, it is crucial and interesting to analyze how the accuracy of localization is affected by the sensor errors and the sensor network, which may help to provide guideline on choosing the specification of sensors and designing the sensor network. To this end, we make theoretical analysis for the different methods based on three types of measurement noise: bounded noise, uniformly distributed noises, and Gaussian white noises. Simulation results illustrate the performance comparison of these different methods.

Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements

“…In this paper, four ToA-based positioning algorithms were evaluated under different conditions (e.g., environments with different propagation conditions, static and dynamic target, and with or without NLOS influence due to the human body). Unlike the performance comparisons performed by other works, where tens of ranging measurements are used for the positioning process [ 38 , 39 , 40 , 41 ], in this work only three ranging measurements were used to assess the performance of the algorithms. This is especially important during the emergency responders’ missions, since the availability of radio signals is very low and there is a high demand for a high localization accuracy [ 2 , 4 , 6 , 8 , 24 , 55 ].…”

“…Therefore, the goal of this paper is to evaluate and compare different positioning algorithms and select the one that best suits in such scenario. So, based on the IPS requirements defined in [ 8 ], the performance assessment of the algorithms is conceived as follows: High Performance with Low Ranging Measurements—unlike Wireless Sensor Networks (WSNs) applications, where tens of ranging measurements can be available [ 38 , 39 , 40 , 41 ], during emergency responders’ missions the availability of radio signals is generally low. This happens due to the following reasons: no reliable infrastructure exists in a building capable of computing the emergency responders’ position, the deployment cannot interfere with the emergency responder’s activities, the low penetration capability of UWB signals in indoor environments (up to 40 m in NLOS scenarios), and the risk of some anchor nodes being destroyed by the fire or falling debris.…”

Optimized indoor positioning for static mode smart devices using BLE

“…In addition, feedback filter considers the previous RSSI measurement to make sure the RSSI can be smoothed. Formula (5) shows this solution: RSSI = α RSSI + (1 − α) RSSI (5) In this equation, k is the current measurement, whereas k-1 is the previous measurement.…”

“…They are placed at fixed positions as followed: iBeacon_1 (coordination 0, 0), iBeacon_2 (0, 5), iBeacon_3 (5, 5) and iBeacon_4 (5, 0). There are five different positions of devices Position A (0, 3), Position B (2, 4), Position C (1, 1), Position D (3, 2), Position E (5,5). There are ten tables randomly placed around the testbed but all devices can see each other directly.…”

“…The Received Signal Strength Indicator (RSSI) then can be measured and used to estimate the distance between the beacons and the mobile devices. Once the distance is known, the position of the devices can be calculated using appropriate techniques such as Least Square Estimation [5][6] or Trilateration-Centroid [7]. These methods require very accurate and stable RSSI and distance measurement to achieve good results.…”