Robust Structured Sparsity-Based Fused Lasso Estimator With Sensor Position Uncertainty

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 usingl1-norm, i.e.,l1-SRA. However, thel1-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 brief. 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 this brief, 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.