![]() ![]() Signal reflection, multipath propagation, noise and signal scattering have great influence on the received RSSI. RSSI value heavily depends on the propagation channel. RSSI-based algorithms have the following characteristics: low power consumption, simple hardware, and high sensitivity to environment. There are many range-based localization techniques, such as those based on time of arrival (TOA), time difference of arrival (TDOA), and received signal strength indicator (RSSI). Compared to range-free localization, range-based localization provides higher precision. ![]() Generally, localization algorithms in WSNs can be divided into two categories: range-based localization and range-free localization. In some position sensitive applications, node localization is essential to the whole network. Localization is a hot issue in wireless sensor networks (WSNs). In addition, a tracking test is performed to validate the effectiveness of the proposed tracking strategy. Compared with the existing RSSI-based algorithms, the proposed tracking strategy exhibits better localization accuracy but consumes more calculation time. The simulation results show a good behavior of the proposed tracking strategy in the presence of space-time variation of the propagation channel. Quantitative criteria are given to guarantee the efficiency of the proposed tracking strategy by providing a trade-off between the grid resolution and parameter variation. Based on practical data acquired from a real localization system, an experimental channel model is constructed to provide RSSI values and verify the proposed tracking strategy. In the context of applications where the positions are constrained on a grid, a novel tracking strategy is proposed to determine the real position and obtain the actual parameters in the monitored region. To estimate channel model parameters, least mean squares method (LMS) is associated with the trilateration method. In this paper, a received signal strength indicator (RSSI)-based parameter tracking strategy for constrained position localization is proposed. ![]()
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