Multi-sensor imaging systems have a very important role and wide applications in surveillance and security systems. In many applications, it is necessary to use an optical protective window as an optical interface connecting the imaging sensor and object of interest’s space; meanwhile an imaging sensor is mounted in a protective enclosure, providing separation from environmental conditions. Optical windows are often used in various optical and electro-optical systems, fulfilling different sometimes very unusual tasks. There are lots of examples in the literature that define optical window design for targeted applications. Through analysis of the various effects that follow optical window application in connection with imaging systems, we have suggested a simplified methodology and practical recommendation for how to define optical protective window specifications in multi-sensor imaging systems, using a system engineering approach. In addition, we have provided initial set of data and simplified calculation tools that can be used in initial analysis to provide proper window material selection and definition of the specifications of optical protective windows in multi-sensor systems. It is shown that although the optical window design seems as a simple task, it requires serious multidisciplinary approach.

This paper presents an analysis comparing theoretical model predictions and experimental measurements of the number of pixels on target (PoT) for unmanned aerial vehicles (UAVs) using multi-sensor electro-optical and infrared (EO/IR) systems. The primary objective is to evaluate the fidelity of theoretical range prediction models that use the number of PoT as a metric, comparing these calculated values against experimental field measurements. The data used in this analysis were acquired using both visible (VIS) and mid-wave infrared (MWIR) channels, with the target UAV positioned at known reference distances up to 900 m. The results quantitatively demonstrate the deviations between the two methodologies, providing critical insights into the practical limitations of performance prediction models for early drone detection systems operating under field conditions.