Research areas
- Target tracking and multitarget estimation
- Switching-state estimation and hybrid systems (IMM, GPB)
- Data association under measurement-origin uncertainty (PDAF, JPDAF, MHT)
- Sensor fusion for radar and sonar systems
Key papers
- mazor-imm-target-tracking-survey — IMM survey (with Mazor, Averbuch, Dayan), 1998.
Recent work
(To be filled by future ingests.)
Collaborators
- E. Mazor
- A. Averbuch
- J. Dayan
- H. A. P. Blom (Blom & Bar-Shalom 1988, original IMM paper)
My notes
Founding figure of modern target tracking. The Bar-Shalom textbooks (“Estimation with Applications to Tracking and Navigation”, 2001; “Tracking and Data Association”, 1988) are the canonical references for the field, and the IMM algorithm — co-developed with Blom in 1988 — became the de facto standard hybrid-state estimator for maneuvering-target tracking by the late 1990s. For the present asset-pricing project, the relevance is the IMM/RBPF contrast: both exploit the same Rao-Blackwellization (continuous state is linear-Gaussian conditional on the regime path), but IMM uses deterministic moment-matching at every step while RBPF uses stochastic sampling-with- resampling in regime-history space. The Bar-Shalom corpus is the right starting point for understanding the cost/accuracy tradeoffs of every switching-state filter ever proposed.