Name
Title | ||
|---|---|---|
A Bernoulli Filter Approach To Detection And Estimation Of Hidden Markov Models Using Cluttered Observation Sequences |
Abstract | ||
|---|---|---|
Hidden Markov Models (Timms) are powerful statistical techniques with many applications, and in this paper they are used for modeling asymmetric threats. The observations generated by such Timms are generally cluttered with observations that are not related to the HMM. In this paper a Bernoulli filter is proposed, which processes cluttered observations and is capable of detecting if there is an HMM present, and if so, estimate the state of the HMM. Results show that the proposed filter is capable of detecting and estimating an HMM except in circumstances where the probability of observing the HMM is lower than the probability of receiving a clutter observation. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Hidden Markov model, detection, estimation, random finite sets, Bernoulli filter |
Field | DocType | ISSN |
Markov process,Maximum-entropy Markov model,Forward algorithm,Pattern recognition,Markov model,Computer science,Markov chain,Speech recognition,Artificial intelligence,Variable-order Markov model,Hidden Markov model,Hidden semi-Markov model | Conference | 1520-6149 |
Citations | PageRank | References |
2 | 0.40 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Karl Granström | 1 | 356 | 24.53 |
| Peter Willett | 2 | 1962 | 224.14 |
| Yaakov Bar-Shalom | 3 | 460 | 99.56 |