Implementing nonparametric approaches like Wilcoxon's detector in real-world applications may present several challenges that need to be addressed:
Computational Complexity: Nonparametric methods often require intensive computational resources due to their data-driven nature and lack of assumptions about the underlying distribution. Efficient algorithms and hardware optimization may be needed to handle this increased computational load effectively.
Data Requirements: Nonparametric detectors typically rely on larger sample sizes for accurate estimation of statistics without assuming specific distributions. Ensuring sufficient data availability can be challenging, especially in scenarios with limited training samples or high variability.
Threshold Selection: Setting appropriate decision thresholds for nonparametric detectors can be challenging as they are not based on predefined models but rather empirical observations from the data itself. Fine-tuning these thresholds for optimal performance without compromising false alarm rates requires careful calibration.
Interpretability: Nonparametric approaches may lack interpretability compared to parametric methods since they do not provide explicit parameter estimates or model assumptions. Understanding how decisions are made based on raw data alone can pose challenges for users seeking transparency in the detection process.
Addressing these challenges through advanced algorithm development, efficient computation strategies, adequate data collection practices, and user-friendly interfaces will be key to successfully implementing nonparametric approaches in practical radar systems.