Abstract
Source localization based on signal strength measurements has become very popular due to its wide applications. This paper focuses on differential received signal strength-based localization with model uncertainties in case of unknown transmit power. The error caused by the measurement noise and the reference power offset and the first-order approximate error generated in the process of DRSS receiving power linearization are merged into the constrained total least square regression matrix equation after linearization. The location optimization problem is formed by minimizing the influence of the errors. Based on the explicit closed form expressions of the first step vector and the second derivative matrix of the optimization problem, an iterative technique based on Newton’s method is proposed. The simulation results show that the root mean square error of the new algorithm is closer to the Cramér–Rao lower bound.
Highlights
With the rapid development of Internet of things, source localization [1, 2] is the basis and the most important supporting technology in a wireless sensor network; it is one of the hottest research directions.A wireless sensor network (WSN) is a kind of distributed network; its terminals are many low-cost sensor nodes
Monte Carlo simulations have been provided to evaluate the performance of the proposed algorithm DRSS-based constrained total least square (CTLS) location algorithm by comparing with weighted least square (WLS) and 2-step WLS algorithm which implicitly exploit the relationship between the extra variable and source location [12]
It can be known that under the same error condition, that is, δ2 = 0:1, by comparing the root mean square error obtained by CTLS and other methods, it can be found that when the number of anchor nodes N is 10, the performance of CTLS is significantly better than that of other methods
Summary
With the rapid development of Internet of things, source localization [1, 2] is the basis and the most important supporting technology in a wireless sensor network; it is one of the hottest research directions.A wireless sensor network (WSN) is a kind of distributed network; its terminals are many low-cost sensor nodes. With the rapid development of Internet of things, source localization [1, 2] is the basis and the most important supporting technology in a wireless sensor network; it is one of the hottest research directions. Source localization refers to the technology of determining the location of unknown target nodes through predeployed distributed sensor nodes with known location information. By using the characteristic that the sound energy attenuation is inversely proportional to the square of the distance from the sound source, Li and Hu in [10] established a RSS model-based target source localization problem and proposed a least square implementation algorithm. In RSS model-based localization methods, the reference power between each anchor node and target node is known. In many application environments, the nodes between each anchor node and target node is not fully cooperative, so the assumption for the known reference power is difficult to be satisfied
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