The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Posterior Cramer-Rao bounds for discrete-time nonlinear filtering
Open accessAbstract
A mean-square error lower bound for the discrete-time nonlinear filtering problem is derived based on the van Trees (1968) (posterior) version of the Cramer-Rao inequality. This lower bound is applicable to multidimensional nonlinear, possibly non-Gaussian, dynamical systems and is more general than the previous bounds in the literature. The case of singular conditional distribution of the one-step-ahead state vector given the present state is considered. The bound is evaluated for three important examples: the recursive estimation of slowly varying parameters of an autoregressive process, tracking a slowly varying frequency of a single cisoid in noise, and tracking parameters of a sinusoidal frequency with sinusoidal phase modulation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hewer, G. (1971) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1971.1099755 · Cited by 326
Cited by
Cognitive Radar Framework for Target Detection and Tracking
Bell, K. L., Baker, C. J., Smith, G. E., Johnson, J. T., & Rangaswamy, M. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2015.2465304 · Cited by 283
Ebrahimian, H., Astroza, R., Conte, J. P., & de Callafon, R. A. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2016.02.002 · Cited by 134
Ebrahimian, H., Astroza, R., Conte, J. P., & Papadimitriou, C. (2018) · Structural Control and Health Monitoring · doi:10.1002/stc.2128 · Cited by 103
Geometrical kinematic modeling on human motion using method of multi-sensor fusion
Xu, C., He, J., Zhang, X., Yao, C., & Tseng, P.-H. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.09.014 · Cited by 58
Metadata
- Authors
- Tichavsky, P., Muravchik, C. H., & Nehorai, A.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1998-05
- Volume
- 46
- Issue
- 5
- Pages
- 1386-1396
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2104130465
- Cited by
- 1,435
- References
- 1
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Posterior Cramer-Rao bounds for discrete-time nonlinear filtering. https://app.cochat.ai/papers/10.1109/78.668800
- The paper
- Tichavsky, P., Muravchik, C. H., & Nehorai, A. (1998). Posterior Cramer-Rao bounds for discrete-time nonlinear filtering. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.668800
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
