Resource allocation for dynamic parameter estimation in parallel channels

Any sensing task is constrained by the resources that the sensor has at its disposal. For example, a radar system can only allocate a certain amount of transmit energy per unit time to track all necessary target parameters. When multiple dynamic parameters must be tracked over time, a common approach is to schedule updates at regular intervals, subject to all of the tasks assigned to the radar timeline. However, with the advent of much more agile sensors, such as all-digital apertures, it is possible to conceive of increasingly adaptive and dynamic resource allocation strategies that will yield better performance in target-dense, resource-constrained scenarios. In this study, the authors investigate adaptive resource allocation in a canonical scenario by assuming a parallel, linear channel model, and derive various allocation strategies and compare their short- and long-term performance. The authors also investigate effectiveness as a function of varying circumstances, such as varying signal-to-noise ratio and strength of the target dynamics, and relate the results to adaptive allocation strategies for phased arrays and all-digital apertures.