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What is Detection and Estimation?
Detection: Say whether there is a target or not at the specified cell.
Estimation: Determine range R, velocity v of the target
Estimation Theory
Level I: Known signals in noise
Level II: Signals with unknown
parameters in noise
Level III: Random signals in noise
Detection Theory
Level I: Known signals in noise
Level II: Signals with unknown
parameters in noise
Level III: Random signals in noise
Detection and Estimation
Classical: Hypothesis/parameters are
fixed, non random (deterministic)
Bayesian: Hypothesis/parameters are treated as random variables
wish assumed priors (or a priori distributions)
Application Areas: Radar, Sonar, Speech, Image Analysis,
Biomedicine (medical), communications, control,
seismology, astronomy
Possible approaches for designing good detection and estimation systems using statistical methods:
1. Structured approach
2. Non-structured approach
Estimation Problem: Extract parameter values from a discrete-time waveform or a data set.
Example-1:
Example-2:
