Platform Load Status Detection with Time-of-Flight Likelihood Testing

The patent transforms the detection problem from direct physical measurement to statistical parameter analysis. By modeling the relationship between sensor measurements and load status through probability distributions and likelihood ratios, the system achieves higher detection accuracy while maintaining computational feasibility. The statistical parameters (mean, variance, likelihood ratios) serve as intermediate representations that capture the essential information without requiring complex physical models.

The patent replaces conventional mechanical or simple electronic detection systems with a statistical computing approach. Instead of using complex hardware to directly detect load status, the system uses software-based statistical analysis (likelihood ratio tests, hypothesis testing) on sensor data to determine cargo presence, achieving high accuracy through algorithmic processing rather than hardware complexity.

The patent segments the detection process into distinct statistical steps: (1) collecting sensor measurements, (2) computing likelihood ratios for each measurement, (3) aggregating likelihood ratios through multiplication, (4) comparing the product to a threshold. This segmentation transforms a complex statistical problem into a series of simple, computationally efficient operations that can be executed sequentially without requiring heavy computational resources.

The patent uses a sequential likelihood ratio test that can terminate early when sufficient evidence is accumulated. By monitoring the cumulative likelihood ratio during the computation process, the system can stop collecting and processing measurements once the threshold is clearly exceeded, avoiding unnecessary computational effort while maintaining high detection accuracy. This partial action approach processes only the minimum required data to achieve reliable results.