On-Line RSSI-Range Model Learning for Target Localization and Tracking

José Ramiro Martínez-De Dios,Francisco Fernández,Anibal Ollero,Carolina Regoli

doi:10.3390/jsan6030015

José Ramiro Martínez-De Dios, Francisco Fernández + Show 2 more

Open Access

https://doi.org/10.3390/jsan6030015

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Abstract

The interactions of Received Signal Strength Indicator (RSSI) with the environment are very difficult to be modeled, inducing significant errors in RSSI-range models and highly disturbing target localization and tracking methods. Some techniques adopt a training-based approach in which they off-line learn the RSSI-range characteristics of the environment in a prior training phase. However, the training phase is a time-consuming process and must be repeated in case of changes in the environment, constraining flexibility and adaptability. This paper presents schemes in which each anchor node on-line learns its RSSI-range models adapted to the particularities of its environment and then uses its trained model for target localization and tracking. Two methods are presented. The first uses the information of the location of anchor nodes to dynamically adapt the RSSI-range model. In the second one, each anchor node uses estimates of the target location –anchor nodes are assumed equipped with cameras—to on-line adapt its RSSI-range model. The paper presents both methods, describes their operation integrated in localization and tracking schemes and experimentally evaluates their performance in the UBILOC testbed.

Highlights

The Received Signal Strength Indicator (RSSI) is a main metric to estimate Quality of Service (QoS) of wireless links [1]
RSSI can be measured by the radio modules of most Commercial Off The Shelf (COTS) Wireless Sensor Network nodes with negligible cost in energy, delay and computational effort
This paper presents methods that address the learning of RSSI-range characteristics of the environment as an on-line process, i.e., training is performed during localization, enabling high adaptability to scenario changes

Summary

Introduction

The Received Signal Strength Indicator (RSSI) is a main metric to estimate Quality of Service (QoS) of wireless links [1]. One of the main drawbacks of using RSSI to estimate WSN link QoS in general—and to estimate the distance between two WSN nodes—is that the strength of radio signals is highly affected by reflections and other interactions of the radio signal with the environment. These interactions are very difficult to be modeled and highly disturb the accuracy of RSSI-based localization techniques. Many methods, e.g., range-free localization methods, compute location estimates without transforming RSSI into distance and avoiding the inaccuracies of RSSI-range models

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Discussion

Conclusion