The Optimization Model of Target Recognition Based on Wireless Sensor Network

In the application of opportunistic networking in wireless sensor network, the technology of target recognition is very important. However, since the sensor reports are typically inconsistent, incomplete, or fuzzy, the technology of target recognition whereby sensor reports is a major challenge. In this paper, based on the minimization of inconsistencies among the sensor reports, a new optimization model of target recognition is presented by using a convex quadratic programming (QP) formulation. Firstly, the description method of sensor report is introduced and then we talk about how to set up this new optimization model of target recognition by using the wireless sensor network reports and how to calculate the solution of this new optimization model. Finally, theory analysis and numeric simulation indicate that this optimization model can generate reasonable fusion results, which is similar to the Dempster-Shafer (D-S) evidence inference model. Furthermore, in contrast to D-S evidence inference model, this optimization model can fuse sensor reports of the form more general than that allowed by the D-S evidence inference model without additional processes. Meantime, it can deal with the high conflict sensor reports.