Received signal strength difference–based tracking estimation method for arbitrarily moving target in wireless sensor networks

Abstract

The surveillance system, which is mainly used for detecting and tracking moving targets, is one of the most significant applications of wireless sensor networks. Up to present, received signal strength indicator is the most common measuring mean for estimating the distance in sensor networks. However, in the presence of noise, it is impossible to gain the accurate distance based on received signal strength indicator. In this article, we propose a new tracking scheme based on received signal strength difference, which is the difference value of received signal strength indicators between two neighboring sampling steps. Supposing the noise has a certain degree of correlation in a certain time interval, received signal strength difference can effectively reduce the negative impact from noise. The tracking algorithm based on received signal strength difference is built: The sensor nodes collectively estimate a possible zone of the target via the signs of received signal strength difference. Next, the possible zone is further immensely shrunk to the refined zone via the absolute values of received signal strength difference. Finally, we determine the target's final location by choosing the reference dot with the minimum norm in the refined zone. The simulation results demonstrate that the proposed tracking method achieves higher localization accuracy than the typical received signal strength indicator-based scheme. The received signal strength difference-based method also has good generality and robustness with respect to the noises with different deviation values and the target following arbitrarily state model.

RSS-Based Cooperative Localization and Orientation Estimation Exploiting Antenna Directivity

“…Methods that include the possibility to receive OLOS measurements in the modelling process can counteract the degradation due to multipath and path-loss variations [30], [31]. A different approach for achieving a more robust RSS-based localization takes additional effects like the radiation pattern of the antennas into account [32]- [34].…”

“…With the BIC given in (36), we can calculate the odds ratio in (34), assuming that all models are equally probable. Note that since the models differ only in terms of the antenna pattern, finding the most probable model is equivalent to finding the most probable antenna pattern (see (2)).…”

RSS-based Cooperative Localization and Orientation Estimation Exploiting Directive Antenna Patterns

“…Methods that include the possibility to receive OLOS measurements in the modelling process can counteract the degradation due to multipath and path-loss variations [30], [31]. A different approach for achieving a more robust RSSbased localization takes additional effects like the radiation pattern of the antennas into account [32]- [34].…”

Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation

“…Despite the lack of precision, RSSI is widely used today in most of WSN localization algorithms [10]. A comprehensive analysis of basic principles and techniques used in the localization algorithms, categories of these algorithms and localization schemes has been covered in [11,12]. Although these methods are affected by the subject of this this paper, they have not been benchmarked or detailed throughout our study and make part of further research.…”