Mission Analysis

Mission analysis is the process of quantifying the system parameters and the resulting performance. A particularly important subset of mission analysis is mission utility analysis, described in Sec. 3.3, which is the process of quantifying how well the system meets its overall mission objectives. Recall that the mission objectives themselves are not quantitative. However, our capacity to meet them should be quantified as well as possible in order to allow us to make intelligent decisions about whether and how to proceed. Mission requirements, introduced in Chap. 1 and discussed in more detail in Chap. 4, are the numerical expressions of how well the objectives must be met They represent a balance between what we want and what is feasible within the constraints on the system and, therefore, should be a central part of the mission analysis activity. In practice, mission analysis is often concerned with how and how well previously defined mission requirements can be met In principle, mission analysis should be the process by which we define and refine mission requirements in order to meet our broad objectives at minimum cost and risk.

A key component of mission analysis is documentation, which provides the organizational memory of both the results and reasons. It is critical to understand fully the choices made, even those which are neither technical nor optimal. We may choose to apply a particular technology for political or economic reasons, or may not have enough manpower to investigate alternatives. In any case, for successful analysis, we must document the real reasons so others can reevaluate them later when the situation may be different Technical people often shy away from nontechnical reasons or try to justify decisions by exaggerating their technical content For example, we may choose for our preliminary FireSat analysis a circular orbit at 1,000 km at an inclination of 60 deg because this is a good mid-range starting point If so, we should document this as the reason rather than trying to further justify these parameters. Later, we or others can choose the best altitude and inclination rather than having to live by choices for which there is no documented justification.

3.2.1 The Mission Analysis Hierarchy

I like to think of the mission analysis process as a huge electronic spreadsheet model of a space system. On the left side of the spreadsheet matrix are die various parameters and alternatives that one might assess, such as power, orbit, number of satellites, and manning levels for ground stations. Along the bottom row are the system's quantitative outputs, indicating its performance, effectiveness, cost, and risk. The matrix itself would capture the functional relationships among the many variables. We would like to wiggle any particular parameter, such as the diameter of the objective in a detector lens or the number of people assigned to the ground station, and determine the effect on all other parameters. In this way, we could quantify the system's performance as a function of all possible variables and their combinations.

Fortunately for the continuing employment of mission analysts, the above spreadsheet model does not yet exist* Instead, we analyze as many reasonable alternatives as possible so we may understand how the system behaves as a function of the principal design features—that is, the system drivers. This approach does not imply that we are uninterested in secondary detail, but simply recognizes that the mission analysis process, much like the space system we are attempting to analyze, is ultimately limited in both cost and schedule. We must achieve the maximum level of understanding within these limits.

If the resources available for concept exploration are limited, as is nearly always the case in realistic situations, then one of the most critical tasks is to intelligently limit the scope of individual analyses. We must be able to compute approximate values for many parameters and to determine at what level of detail we should reasonably stop. In practice, this is made difficult by the continuing demand for additional detail and depth. Thus, we must be able to determine and make clear to others what elements of that detail will significantly affect the overall system performance and what elements, while important, can reasonably be left to a more detailed design phase.

We use two main methods to limit the depth of analysis in any particular area. The first is to clearly identify each area's system drivers by the methods in Sec. 2.3 and to concentrate most of the mission analysis effort on these drivers. The second is to clearly identify the goal of the system study and to provide a level of detail appropriate to that goal. This approach leads to a mission analysis hierarchy, summarized in Table 3-2, in which studies take on increased levels of detail and complexity as the activity progresses. The first three types of studies are meant to be quick with limited detail and are not intended to provide definitive results. The last three are much more complex ways to select an alternative to provide the best system performance.

* The Design-to-Cost model at JPL [Shishko, 1996] and similar models being developed throughout the aerospace community are attempting to automate this basic design process of evaluating the system-wide implication of changes. In due course, system engineers may become technologically obsolete. Much like modem chess players, the challenge to future system engineers will be to stay ahead of the computer in being creative and innovative.

TABLE 3-2. The Mission Analysis Hierarchy. These help us decide how much detail to study during the preliminary design phase.

Analysis Typo

Goal

Feasibility Assessment

To establish whether an objective Is achievable and Its approximate degree of complexity

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