Adaptive selection of sensors based on individual performances in a multisensor environment

An important issue in the fusion of multisensor data in the context of scene interpretation and multitarget tracking is the ability to evaluate and characterize sensor performance and establish confidence factors for the individual sensors. This paper presents a methodology for adaptively determining sensor confidence factors based upon sensor performance as measured by the degree of consensus among the various sensors. The fusion process is based upon evidential reasoning and statistical clustering which utilize the sensor confidence factors. The sensor confidence factors are based upon sensor characteristics, environmental conditions, and sensor performance. The individual sensor performance is derived in terms of the fusion results and the degree of consensus between the individual sensor data and the fusion data. Experimental results are presented to illustrate the technique and to demonstrate the effectiveness of the methodology in scene interpretation.