Time-Parameterized Sensing Task Model for Real-Time Tracking Min-Young Nam, Chang-Gun Lee * ECE Dept. The Ohio State University Columbus, OH {namm, cglee}@ece.osu.edu Kanghee Kim CSE Dept. Seoul National University Seoul, Korea khkim@archi.snu.ac.kr Marco Caccamo CS Dept. University of Illinois Urbana-Champaign, IL mcaccamo@cs.uiuc.edu Abstract This paper proposes a novel task model whose phys- ical and temporal parameters are specified as time- parameterized functions and their values are finally de- termined at the actual dispatch time. This model is clearly differentiated from the classical task model where parame- ters are fixed at the job release time. The new model better suits sensing tasks in tracking applications, since the sensor parameters such as field-of-view and measurement duration can be properly adjusted at the actual sensing time. The new model, however, creates the cyclic dependency between task parameters and scheduling behavior, that is, the task param- eters depend on scheduling behavior and the latter in turn depends on the former. This cyclic dependency makes the schedulability check even more difficult. We handle this dif- ficulty by iterative convergence and probabilistic schedu- lability envelope, which provides an efficient online schedu- lability check. The experimental study shows that the new model significantly improves the effective capacity of track- ing systems without losing track accuracy. 1 Introduction Real-time tracking of physical states is a typical example of real-time applications. This includes pure tracking appli- cations like radar-based target tracking and also plant state tracking for supporting the final control of the plant. For the success of such a tracking, a set of associated temporal con- straints should be guaranteed. The real-time community has traditionally assumed that tasks’ temporal parameters such as periods, execution times and deadlines are given by domain experts either in determin- istic or probabilistic forms and mainly focused on respecting them. However, relatively less attention has been paid to un- derlying nature of real-time constraints driven by the appli- cation algorithms such as tracking and control algorithms. Recent papers [4, 6, 8, 15, 19] studied the joint-relations * The corresponding author is Chang-Gun Lee. between control and scheduling and pointed out that the end- performance largely depends on physical interaction points, i.e., sensing and actuation times rather than job scheduling times on CPU. To improve the control performance, they proposed a new scheduling method that separates sensor- read and actuator-write from the regular control-law com- putation and executes the read/write operations at the desig- nated periodic points regardless of scheduling jitters of com- putational jobs. This solution is possible because the time for a read/write operation is negligibly small if we use a ded- icated continuous sensor/actuator. However, in more general tracking applications, we usu- ally use a multifunctional shared sensor like a radar antenna or an infrared camera for tracking multiple targets concur- rently. For those sensors, sensing is not a simple read op- eration but a time-taking job for steering sensor’s field-of- view (FOV) and also for measuring the field-of-view for a certain time to capture a measurement of an acceptable qual- ity. If such a sensor is shared by multiple concurrent track- ing tasks, multiple sensing jobs can be released at the same time and thus they will be queued and scheduled causing non-negligible deviations from the designated sensing times. Therefore, we have to address such deviations by scheduling multiple sensing jobs while jointly considering the nature of tracking algorithms. However, the current tracking system design relies on extensive simulations to achieve acceptable end-performance without analytic reasoning, due to limited knowledge of real-time scheduling. This paper jointly considers the tracking algorithm and sensing job scheduling to build an analytic co-design frame- work. The contribution can be summarized as follows: • We propose a novel task model called a time- parameterized model, which better suits tracking appli- cations. Unlike classical real-time task models where each job’s parameters are fixed at its release time, in the new model, each sensing job’s parameters like FOV and measurement duration are given as time-varying func- tions. With the time-varying functions, the actual pa- rameters are finalized when the job is eventually dis-