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In the future, wireless power transmission is expected to cover power levels ranging from milliwatts to megawatts and over distances of a few meters to kilometers for both stationary and moving photovoltaic (PV) targets. Optical wireless power transmission (OWPT) is a promising solution for such multi-scale systems, which use the narrow beam divergence of light. To enhance the efficiency of power generation in PV targets, real-time detection of the target’s position and attitude is crucial for OWPT systems. The authors propose detecting the PV using differential absorption images as one such method. This paper investigates algorithms for tracking moving PV targets and evaluates their performance. The first algorithm combines thresholds with an autoregressive (AR) model, while the other two use estimation with Kalman filters. The comparison of tracking algorithms can be undertaken using a score function based on the position estimation error. The evaluation results indicate that the AR model combined with thresholds, on average, outperforms the other models. There was no significant difference between the approach involving use of a Kalman filter to estimate positions based on a uniform motion model and the approach involving use of a Kalman filter to estimate the AR model. The authors’ series of studies, from a former high-level requirement study to the current target tracking using differential absorption imaging, has verified a concept for one of the crucial processes in OWPT. These works form a step toward the practical implementation of OWPT systems.
Asaba et al. (Wed,) studied this question.