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Fig. 12 Impact of the beacon structure.

The reason is that the same beacon structure is used regardless the network density.

Note that the increase of the beacon structure could generate a communication overhead.

These procedures require the modification of the periodic beacon structure, that we present later.

The recursion direction and the beacon structure are depicted in Fig. 2a.

To ensure that the recursion starts at a single point, the algorithm uses a fixed beacon structure.

However, the DPE may benefit from multiple beacon structures [24].

Thus, to evaluate the performance of the target tracking algorithms with multiple beacon structures, we vary the number of such structures from 1 to 5.

Besides, when we use of 50 distance estimates, the average tracking error converges to 0.6 m regardless of the number of beacon structures available (Fig. 12a, b).

With 50 distance estimates, the Kalman Filter outperforms the Particle Filter with any number of beacon structures, because the non-Gaussian noise introduced by the location is very low (Fig. 12(d)).

By performing a single distance estimate in the localization process (Fig. 12(c)) and using a three-beacon structure, the tracking results with Kalman and Particle filters are very close.