Object Detection Device Using Signal Interpolation and Noise Removal

The signal conversion process is segmented into multiple stages: first converting optical signals to electrical signals using photodetectors, then converting to digital signals using ADCs, and finally performing interpolation processing. This segmentation allows each stage to be optimized independently, achieving high distance resolution without requiring an overly complex single-stage converter.

The patent introduces a temporal dimension by performing interpolation processing on the digitized signals. By creating additional signal samples between existing samples through interpolation, the system effectively increases the sampling rate and improves distance resolution without adding physical conversion components, thus resolving the contradiction between precision and complexity.

The patent applies preliminary action by performing interpolation processing before noise removal. By first creating a more densely sampled signal through interpolation, the system prepares the signal in advance to make subsequent noise removal more effective. This preliminary processing enables better differentiation between signal and noise components.

The noise removal process uses feedback mechanisms where the processed signal is continuously evaluated and adjusted. The system compares the interpolated signal with expected signal characteristics and iteratively removes noise components, allowing adaptive noise suppression that improves reliability while managing processing complexity through intelligent algorithms.

The patent applies partial action by performing interpolation only at critical stages where distance measurement precision is most important, rather than uniformly across the entire signal processing pipeline. By selectively applying interpolation to the most impactful portions of the signal, the system achieves improved distance resolution while minimizing the overall processing time penalty.

The system dynamically adjusts interpolation parameters such as the interpolation factor and processing resolution based on the specific measurement requirements and available computational resources. By changing these parameters adaptively, the system can achieve high distance resolution when needed while reducing processing time for less critical measurements, thus managing the time-resolution trade-off.