LAUSR

Electrical resistance tomography (ERT) is a sensing technique for process monitoring and measurement, the image reconstruction algorithms are required fast and accurate. The OMP class algorithm has the property of reconstructing a higher quality signal by fewer iteration steps (shorter elapsed time) and faster convergence speed compared with other iterative algorithms. But the classical OMP is not self-adapted and may fall into a local optimal solution. In this paper, a modified orthogonal matching pursuit (MOMP) was presented by adding a continuous constraint to prevent local optimal solution and self-adaption process on classical OMP algorithm which finds the optimal number of iterations based on the information entropy of measurement to make it suitable for ERT inverse problem. The algorithm was experimentally validated versus other algorithms through a 16-electrode ERT system. Comparing with other algorithms, MOMP algorithm has a lower mean square error than non-iterative algorithms and a shorter time cost than iterative algorithms. Dynamic experiments showed that the MOMP algorithm is promising for industrial process monitoring.