Abstract
Most papers by past researchers on decentralized multi-sensor detection system, usually employing fixed-value fusion weight coefficients, appear unable to keep the system in optimized detection status when the detection probability is unknown or varying. We propose a feedback adaptive learning algorithm to meet the optimized detection requirement. In the fusion scheme of the adaptive algorithm proposed by us, information system can estimate the Bayes fusion weight coefficients online. In the full paper, we explain in much detail the adaptive algorithm proposed by us; here we just list the three topics discussed in our detailed explanation; (1) adaptive algorithm; (2) analysis of convergence of fusion weight coefficients; one important result is that, under certain conditions, the fusion weight coefficients will converge to their optimized values; (3) error analysis. Finally we give a numerical simulation example; the variations of fusion weight coefficients with number of iterations are shown in Fig. 1 of the full paper. Fig. 1 shows that almost all fusion weight coefficients converge to their optimized values after about 1500 iterations.
| Original language | English |
|---|---|
| Pages (from-to) | 143-146 |
| Number of pages | 4 |
| Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
| Volume | 24 |
| Issue number | 2 |
| State | Published - Apr 2006 |
Keywords
- Adaptive learning algorithm
- Decentralized detection system
- Unknown detection probability