- What is the difference between detection and estimation?
- What is the meaning of estimation theory?
- What are the uses of estimation theory?
- What is signal detection model?
What is the difference between detection and estimation?
Detection: Decision between two (or a small number of) possible hypothesis to choose the best of the two hypothesis. Parameter Estimation: Given a set of observations and given an assumed probabilistic model, we get the best estimate of the parameters of the model.
What is the meaning of estimation theory?
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.
What are the uses of estimation theory?
Applications : Image processing, communications, biomedicine, system identification, state estimation in control, etc. Range estimation : We transmit a pulse that is reflected by the aircraft. An echo is received after τ second. Range θ is estimated from the equation θ = τc/2 where c is the light's speed.
What is signal detection model?
Definition. Signal detection models are probabilistic representations of the structure of the information and the rules for decision-making that can be applied to a specific decision problem.