Thesis - Leigh Moody.pdf - Bad Request - Cranfield University

Chapter 3 / Sensors / Seeker
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When multiple targets appear in the FoV of a single sensor, or a single
target in the FoV of a number of sensors, there is the problem of track
association. This is more complicated with the MSS since each sensor will
generate tracks relating to common targets requiring correlated, a problem
studied by Singer and Kanyuck (1971). Track fusion in Figure 3-44 deals
with the problem of correlated estimation errors as process noise enters from
each sensor.
TARGET
SENSOR 1
TRACK 1
3.9-2
TARGET
SENSOR 2
TARGET
SENSOR "N"
TRACK 2 TRACK "N"
MULTIPLE
TRACK FUSION
OPTIMAL TARGET
TRACK SELECTION
Figure 3-44 : Track Fusion Principle
Measurement fusion in Figure 3-45 maintains optimal tracks using
information from a number of sensors assuming that gating isolates the
targets under observation in a multiple target environment.
TARGET
SENSOR 1
TARGET 1
WITHIN GATE
TARGET
SENSOR 2
TARGET 1
WITHIN GATE
MEASUREMENT
FUSION
OPTIMAL TARGET
TRACK
TARGET
SENSOR "N"
TARGET 1
WITHIN GATE
Figure 3-45 : Measurement Fusion Principle
Measurement fusion gives more accurate target state estimates, sensor
calibration, and a faster response to target manoeuvres, whereas track fusion
is better in cluttered environments and against countermeasures. This
comparison of track and measurement fusion is continued in §5.1.
3.9.2 Description
The seeker model in Figure 3-46 comprises a single gimbal set (outer yaw,
inner pitch gimbals) that can be locked to the missile boresight in strapdown
applications supporting the detector shown in Figure 3-47. It has been
assumed that the mass imbalance between the gimbals is minimised by
careful design, and the pitch and yaw gimbal models do not interact.