Tracking and data association
Elements of information theory
Elements of information theory
An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Mathematics of Data Fusion
Statistical Multisource-Multitarget Information Fusion
Statistical Multisource-Multitarget Information Fusion
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
IEEE Transactions on Signal Processing
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing
Analytic Implementations of the Cardinalized Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing - Part II
A comparison of two Crame´r-Rao bounds for nonlinear filtering with Pd
IEEE Transactions on Signal Processing
Bayesian Filtering With Random Finite Set Observations
IEEE Transactions on Signal Processing
A Consistent Metric for Performance Evaluation of Multi-Object Filters
IEEE Transactions on Signal Processing - Part I
Detection of stochastic processes
IEEE Transactions on Information Theory
Hi-index | 35.68 |
This paper considers the performance limits for joint detection and estimation from a finite set-valued observation that is stochastically related to the state or parameter of interest. Detection refers to inference about the existence of the state, whereas estimation refers to inference about its value, when detected. Since we need to determine the existence/non-existence of the state as well as its value, the usual notion of Euclidean distance error does not jointly capture detection and estimation error in a meaningful manner. Treating the state as set, which can be either empty or singleton, admits a meaningful distance error for joint detection and estimation. We derive bounds on this distance error for a widely used class of observation models. When existence of the state is a certainty, our bounds coincide with recent results on Cramér-Rao bounds for estimation only problems.