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: