1：Experimental Design and Method Selection:

The study proposes a novel design criterion for radar systems that balances the requirements for a large probability of detection, a small probability of false alarm, and a short scan time. The methodology involves optimizing the decision rule and the number of data samples acquired from the same resolution cell (i.e., the time-on-target).

2：Sample Selection and Data Sources:

The study uses a Gaussian model for the received observations, where target responses and noise contributions are modeled as complex circularly-symmetric Gaussian random variables.

3：List of Experimental Equipment and Materials:

The study does not specify particular equipment or materials but focuses on theoretical modeling and optimization.

4：Experimental Procedures and Operational Workflow:

The study involves deriving the likelihood ratio test (LRT) for radar detection and optimizing the detection threshold and the time-on-target to maximize the detection rate under a false alarm rate constraint.

5：Data Analysis Methods:

The study analyzes the performance of the proposed design criterion through theoretical derivations and numerical examples, comparing it with other design criteria such as minimizing the quantile of the detection time or maximizing the cumulative detection probability.