We propose a framework for obtaining statistical inferences from
multi-modal and multi-sensor data. In particular, we consider a
military battlefield scene and address problems that arise in
tactical decision-making while using a wide variety of sensors (an
infrared camera, an acoustic sensor array, a human scout, and a
seismic sensor array). Outputs of these sensors vary widely, from
2D images and 1D signals to categorical reports. We propose novel
statistical models for representing seismic sensor data and human
scout reports while using standard models for images and acoustic
data. Combining the joint likelihood function with a marked
Poisson prior, we formulate a Bayesian framework and use a
Metropolis-Hastings algorithm to generate inferences. We
demonstrate this framework using experiments involving simulated
data.