SMAD

58 Mission Evaluation 3.2
3.3 Step 8: Mission Utility
direct relationship between the altitude of the FrreSat spacecraft, the size of the
payload. the angles at which it works, and the resolution with which it can distinguish
features on the ground.
TABLE 3-5. Common System Algorithms Used for Quantifying Basic Levels of Performance.
These analyses use physical or geometrical formulas to determine how
system performance varies with key parameters.
Where
Algorithm Used For Discussed
UnkBudget Communications and data rate analysis Sec. 13.3.6
Diffraction-limited Aperture sizing for optics or antennas; Sec. 9.3
Optics
determining resolution
Payload Sensitivity Payload sizing and performance estImetes Sees. 9.4, 9.5
Radar Equation Radar sizing and performance estimates [Cantaflo,1989)
Earth Coverage, Coverage assessment; system sizing; Sees. 5.2, 7.2
Area Search Rates performance estImetes
Mapping and Geolocation; Instrument and antenna pointing; Sec. 5.4
Pointing Budget lmege sensing
System algorithms are powerful in that they show us directly how performance
varies with key parameters. However, they are inherently limited because they presume
the rest of the system is designed with fundamental physics or geometry as the
limiting characteristic. For FueSat, resolution could also be limited by the optical
quality of the lens, by the detector technology, by the spacecraft's pointing stability,
or even by the data rates at which the instrument can provide results or that the satellite
can transmit to the ground. In using system algorithms, we assume that we have
correctly identified what limits system performance. But we must understand that
these assumptions may break down as each parameter changes. Fmding the limits of
these system algorithms helps us analyze the problem and determine its key components.
Thus, we may find that a low-cost FrreSat system is limited principally by
achieving spacecraft stability at low cost. Therefore, our attention would be focused
on the attitude control system and on the level of resolution that can be achieved as a
function of system cost
The second method for quantifying performance is by comparing our design with
existing systems. In this type of analysis we use the established characteristics of
existing sensors, systems, or components and adjust the expected performance according
to basic physics or the continuing evolution of technology. The list of payload
instruments in Chap. 9 is an excellent starting point for comparing performance with
existing systems. We could. for example, use the field of view, resolution, and integration
time for an existing sensor and apply them to FJreSat. We then modify the basic
sensor parameters such as the aperture, focal length, or pixel size, to satisfy our mission's
unique requirements. To do this, we must work with someone who knows the
technology, the allowable range of modifications, and their cost For example, we may
be able to improve the resolution by doubling the diameter of the objective, but doing
so may cost too much. Thus, to estimate performance based on existing systems, we
need information from those who understand the main cost and performance drivers
of that technology.
'
The third way to quantify system performance is simulation, described in more
detail in Sec. 3.3.2. Because it is time-consuming, we typically use simulation only for
key performance parameters. However, simulations allow much more complex modeling
and can incorporate limits on performance from multiple factors (e.g., resolution,
stability, and data rate). Because they provide much less insight, however, we must
review the results carefully to see if they apply to given situations. Still, in complex
circumstances, simulation may be the only acceptable way to quantify system performance.
A much less expensive method of .simulation is the use of commercial mission
analysis tools as discussed in Sec. 3.3.3.
3.3 Step 8: Mission Utility
Mission utility tuUllysis quantifies mission performance as a function of design,
cost, risk, and schedule. It is used to (1) provide quantitative information for decision
making, and (2) provide feedback on the system design. Ultimately, an individual or
group will decide whether to build a space system and which system to build based on
overall performance, cost, and risk relative to other activities. As discussed in Sec. 3.4,
this does not mean the decisiori is or should be fundamentally technical in nature.
However, even though basic decisions may be political, economic, or sociological, the
best possible quantitative information from the mission utility analysis process should
be available to support them.
Mission utility analysis also provides feedback for the system design by assessing
how well altemative configurations meet the mission objectives. FJreSat shows how
this process might work in practice. Mission analysis quantifies how well alternative
systems can detect and monitor forest fires, thereby helping us to decide whether to
proceed with a more detailed design of several satellites in low-Earth orbit or a single
larger satellite in a higher orbit As we continue these trades, mission analysis
establishes the probability of being able to detect a given forest fire within a given
time, with and without FrreSat, and with varying numbers of spacecraft. For FireSat,
the decision makers are those responsible for protecting the forests of the United
States. We want to provide them with the technical information they need to determine
whether they should spend their limited resources on FrreSat or on some alternative.
If they select FrreSat, we will provide the technical information needed to allow them
to select how many satellites and what level of redundancy to include.
3.3.1 Performance Parameters and Measures of Etfectiveness
The purpose of mission analysis is to quantify the system's performance and its
ability to meet the ultimate mission objectives. Typically this requires two distinct
types of quantities-performance parameters and measures of effectiveness. Performonee
parameters, such as those shown in Table 3-6 for FrreSat, quantify how well
the system works, without explicitly measuring how well it meets mission objectives.
. Performance parameters may include coverage statistics, power efficiency, or the
resolution of a particular instrument as a function of nadir angle. In contrast, measures
of effectiveness (MoBs) or figures of merit (FoMs) quantify directly how well the
system meets the mission objectives. For FrreSat, the principal MoE will be a numerical
estimate of how well the system can detect forest fJreS or the consequences of
doiJig so. This could. for example, be the probability of detecting a given forest fire
within 6 hours, or the estimated dollar value of savings resulting from early fire detection.
Table 3-7 shows other examples.