Monte Carlo localization algorithm based on particle swarm optimization

Figures & data

Figure 1. Initial sampling region determined by three anchor nodes.

Figure 2. Possible sampling region for different topology.

Figure 3. Sampling region of MCL.

Figure 4. Sampling region of TSMCL-BPSO.

Figure 5. Particle optimization process in PSO algorithm.

Figure 6. Particle optimization process in TSMCL-BPSO algorithm.

Figure 7. Node location procedure.

Table 1. Clustering evaluate matrix.

Parameters	Values
Monitoring area	$200\mathrm{m}\cdot 200\mathrm{m}$
Number of anchor nodes	80
Number of target nodes	20
Node communication radius	50 m
Node moving speed	[0, 70 m/s]
Population size	30
Maximum number of iterations	100
Number of experiments	50
Population size	30
Search dimensions	2
Cognitive learning factors	1.2
Social learning factors	0.5
Initial step size	50
Number of replication	4

Figure 8. Initial nodes distribution.

Figure 9. The value of fitness function in an experiment.

Figure 10. Positioning error in TSMCL-BPSO algorithm.

Figure 11. Relationship between anchor node density and positioning error.

Figure 12. Relationship between maximum moving speed of node and positioning error.

Figure 13. Relationship between the anchor node density and the execution time.