Robust Structured Sparsity Based Fused Lasso Estimator With Sensor Position Uncertainty

• Published in: IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2024-04, Vol.71 (4), p.1-1
• Main Authors: Mishra, Abhijit, Sahoo, Upendra Kumar, Maiti, Subrata
• Format: Article
• Language: English
• Subjects: Computational efficiency; Computing costs; Costs; Estimation; fused lasso; Mathematical models; Position sensing; Radio attenuation; Radio links; Radio tomographic imaging; Radio waves; Robustness; Sensors; Smoothness; Sparsity; spatial loss field; stochastic robust approximation; Support vector machines; support vector regression; Uncertainty; Upper bound; Wave attenuation
• ISSN: 1549-7747; 1558-3791
• DOI: 10.1109/TCSII.2023.3330151

In radio tomographic imaging (RTI), targets induce attenuation of radio waves and cause shadowing of radio links. The radio maps generated due to shadowing phenomena are known as spatial loss fields (SLFs). One of the concerns for SLF estimation is improvement in sparsity. Furthermore, the improvement of sparsity in estimated SLF becomes challenging with uncertain sensor position information. This problem is handled by the stochastic robust approximation (SRA) technique using l1-norm, i.e., l1-SRA. However, the l1-SRA cannot simultaneously enhance the sparsity and smoothness features of the SLF. To handle such a scenario, a robust fused lasso (FL)-based SRA technique, i.e., FL-SRA, is proposed in this letter. However, the proposed FL-SRA estimator has a higher computational cost, which is quadratic with the number of pixels. Therefore, in the second part of the letter, a support vector regression (SVR)-based estimator with sensor position uncertainty, UFL-SVR, is proposed. UFL-SVR has a lower computational cost than FL-SRA, whose cost is quadratic with the number of links. The results of FL-SRA and UFL-SVR are compared to verify the findings.