Which sonar method provides both depth and detection performance predictions?

The HF-STDA (High Frequency - Sonar Target Detection Algorithm) stands out as the correct choice because it is specifically designed to provide predictions on both depth and detection performance. This method utilizes high-frequency signals, which allows for enhanced resolution in determining the depth of a target while simultaneously offering insight into the ability to detect that target under various environmental conditions.

The HF-STDA effectively combines aspects of sonar signal processing and environmental modeling to yield comprehensive performance predictions, making it a valuable tool for underwater navigation and object detection. Moreover, its basis in high-frequency technology enables it to work effectively in shallow water environments, where traditional sonar methods may falter.

Other options, like the AN/SSQ-94 and AN/SQQ-32, have specific capabilities focused on particular sonar applications but do not integrate the same depth and detection performance predictions that the HF-STDA offers. The Classify Array, typically utilized for target classification rather than depth or detection performance predictions, also lacks this dual functionality. Thus, HF-STDA clearly emerges as the most effective method for achieving both depth and detection performance predictions in sonar applications.