pith. sign in

arxiv: 2605.15936 · v1 · pith:TI2SETBWnew · submitted 2026-05-15 · 📡 eess.SY · cs.SY

State Estimation

Hao Li This is my paper

Pith reviewed 2026-05-20 16:09 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords state estimationcontrol theoryadvanced controlpractical applicationscontrol systemsmethodologyknow-why and know-how
0
0 comments X

The pith

State estimation is the most essential mathematical aspect of control systems for practical applications.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that control systems involve hardware, software, operation, maintenance, economy, and society, yet the single most essential aspect in the mathematical sense is control theory. It claims this theory gains its special charm from being deeply rooted in practical applications, where the fusion of know-why and know-how becomes visible. The article therefore focuses on state estimation as the key aspect of advanced control theory for such applications. A reader would care because this framing explains why methodological rigor matters more than isolated technical details when control methods move from theory to real engineering use.

Core claim

Control science is a core part of the third industrial revolution. Control systems raise many considerations, but the aspect most essential in the mathematical sense is methodology, referred to as control theory. Control theory is even more charming because it is deeply rooted in practical applications, with its charms consisting in both know-why and know-how. Their fusion highlights the value of control theory, and the article introduces the state estimation aspect of advanced control theory for practical applications.

What carries the argument

State estimation, treated as the central methodological focus that links mathematical control theory to practical applications by determining system states from measurements.

If this is right

  • Practical control system design must prioritize state estimation methods to realize the fusion of theory and application.
  • Advanced control techniques gain effectiveness when state estimation serves as their methodological foundation.
  • Control theory for real-world use requires explicit attention to how mathematical methodology supports know-how.
  • The charms of control theory become clearest when state estimation bridges abstract models and operational constraints.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Treating state estimation as central may encourage tighter integration between estimation algorithms and real-time hardware constraints.
  • The same emphasis could extend to fields where control systems interact with uncertain data sources, such as sensor networks.

Load-bearing premise

The premise that state estimation qualifies as the most essential aspect because control theory draws its main value from the fusion of theoretical knowledge and practical application.

What would settle it

A set of successful practical control implementations that achieve their goals without relying on state estimation methods would undermine the claim that this aspect is the most essential in the mathematical sense.

Figures

Figures reproduced from arXiv: 2605.15936 by Hao Li.

Figure 1
Figure 1. Figure 1: Landmark distance measurement model In the given example, the landmark distance measurement model described in (3) conveys the causal relationship between the intelligent system state x ≡ [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Methodology of estimation A control system’s target process is observable if its state x can be inferred with its available measurements, be the state inferred directly or indirectly. To better understand observability, it is worth clarifying the difference between unobservability and stochastic uncertainty. In practical applications, the estimate of any entity suffers from stochastic uncertainty that exis… view at source ↗
Figure 3
Figure 3. Figure 3: Recursive estimation: (top) dynamic Bayesian network (DBN) perspective; (bot [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Kalman filter update: weighted average of the predicted estimate [PITH_FULL_IMAGE:figures/full_fig_p022_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Interacting multiple model transition The normalization condition Xm j=1 Cij = 1 holds for each i. It is worth noting that Xm i=1 Cij = 1 does not necessarily hold, though the transition matrix C may be set to satisfy this plausible normalization condition by coincidence in practical applications. Algorithm overview Each recursive cycle of the interacting multiple model algorithm [27] consists of four step… view at source ↗
Figure 6
Figure 6. Figure 6: Trimodal distribution Distributions encountered in reality can hardly be strictly Gaussian, yet the Gaussian distribution assumption is fairly effective for modelling unique-modal distributions which are rather common in practical applications. However, if distributions are not unique-modal, then it is inappropriate to approximate them as Gaussian distributions. For example, given a trimodal distribution i… view at source ↗
Figure 7
Figure 7. Figure 7: Ring-shape distribution mixture of Gaussian distributions p(x) = Xm k=1 αkN(x |xˆk, Σˆ k) = Xm k=1 αkN(xˆk, Σˆ k) (71a) i.e. x ∼ Xm k=1 αkN(x |xˆk, Σˆ k) = Xm k=1 αkN(xˆk, Σˆ k) (71b) where the Gaussian distribution notations N(·, ·) and N(· |·, ·) are defined in (12). In practice, the total number m of Gaussian distributions is usually set empirically. For recursive esti￾mation, not only the state estimat… view at source ↗
Figure 8
Figure 8. Figure 8: Federated Kalman filter The covariance expansion technique specified in (84) is theoretically rooted in the matrix inequality      Σ0 Σ0 · · · Σ0 Σ0 Σ0 · · · Σ0 . . . . . . . . . . . . Σ0 Σ0 · · · Σ0      ≤      1 w1 Σ0 1 w2 Σ0 . . . 1 wN Σ0      . (85) Proof. The matrix inequality (85) is equivalent to ∀xi , · · · , xN X N i=1 X N j=1 x T i Σ0xj ≤ X N i=1 1 wi x T i Σ0xi . (86) Decompo… view at source ↗
Figure 9
Figure 9. Figure 9: Analog electronic circuits realizing the Kalman–Bucy filter example [PITH_FULL_IMAGE:figures/full_fig_p068_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Integrated full-state feedback control with estimator [PITH_FULL_IMAGE:figures/full_fig_p071_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Estimates of the single inverted pendulum state: (top-left) inverted pendulum [PITH_FULL_IMAGE:figures/full_fig_p075_11.png] view at source ↗
read the original abstract

Control science is a core representative of the third industrial revolution and is so important to modern civilization. Control systems are the main subject of control science and may involve many aspects of consideration, such as hardware consideration, software consideration, operation consideration, maintenance consideration, economy consideration, society consideration. However, besides all such aspects of consideration, one aspect that is most essential to the control system is methodology consideration in mathematical sense, knowledge on which is what we refer to as control theory. Besides its importance from the mathematical perspective, control theory is even more charming as it is deeply rooted in practical applications. Charms of control theory consist in both know-why and know-how and it is the fusion of control theory and practical applications that highlights such charms. Control theory for practical applications, especially when somewhat with so-called "advanced" flavour, involves several fundamental aspects. This article introduces the State Estimation aspect of Advanced Control Theory for Practical Applications [1,2].

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript claims that state estimation is the most essential methodological aspect of control systems in the mathematical sense and introduces this topic as part of advanced control theory for practical applications, stressing the fusion of know-why and know-how.

Significance. An introductory overview that clearly positions state estimation within practical control applications could have modest educational value for newcomers to the field. However, the manuscript contains no derivations, algorithms, comparisons, examples, or technical results, so it does not constitute a substantive contribution to the literature even if its framing is accepted.

major comments (2)
  1. [Abstract] Abstract: The central assertion that state estimation is 'most essential to the control system in the mathematical sense' is stated without any supporting argument, comparison to other control-theoretic concepts (e.g., stability margins or controllability), or reference to specific mathematical properties that would justify the ranking.
  2. [Abstract] Abstract: The text references [1,2] as the basis for the introduction but supplies no independent technical content, state-estimation equations, observer designs, or practical application examples, leaving the promised 'introduction' unfulfilled.
minor comments (1)
  1. [Abstract] The phrasing 'somewhat with so-called “advanced” flavour' is imprecise; a brief clarification of which features qualify the theory as advanced would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed review of our manuscript. We provide a point-by-point response to the major comments and outline the revisions we intend to implement in the updated version.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central assertion that state estimation is 'most essential to the control system in the mathematical sense' is stated without any supporting argument, comparison to other control-theoretic concepts (e.g., stability margins or controllability), or reference to specific mathematical properties that would justify the ranking.

    Authors: We acknowledge that the manuscript could benefit from a more explicit justification for positioning state estimation as the most essential methodological aspect in the mathematical sense. The current text emphasizes its role in the fusion of theoretical understanding and practical application. In the revision, we will add a paragraph that briefly contrasts state estimation with other key concepts like controllability and stability analysis, highlighting how state estimation addresses uncertainty in real-world systems through mathematical frameworks such as observability and estimation error bounds. Appropriate references will be included to support this discussion. revision: yes

  2. Referee: [Abstract] Abstract: The text references [1,2] as the basis for the introduction but supplies no independent technical content, state-estimation equations, observer designs, or practical application examples, leaving the promised 'introduction' unfulfilled.

    Authors: The manuscript is conceived as a conceptual introduction that situates state estimation within the broader context of advanced control theory for practical applications, rather than a comprehensive technical survey. Nevertheless, to better meet the expectations of an introduction, we agree to incorporate a high-level overview of fundamental state estimation techniques. This will include a brief description of common approaches like the Luenberger observer and Kalman filter, along with a simple illustrative example of their application in a practical control scenario, while maintaining the focus on the integration of know-why and know-how. These additions will be concise and reference the foundational literature. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The manuscript is an introductory overview framing state estimation as a key aspect of advanced control theory for practical applications. It contains no equations, derivations, predictions, or technical claims that could reduce to inputs by construction. The text makes broad statements about control theory and cites [1,2] to introduce the topic, but these citations support framing rather than load-bearing premises for any result. No self-definitional steps, fitted inputs presented as predictions, or uniqueness theorems appear. As an overview without a falsifiable derivation chain, the paper is self-contained against external benchmarks and exhibits no circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

With only the abstract available, no free parameters, axioms, or invented entities can be identified from the text.

pith-pipeline@v0.9.0 · 5673 in / 872 out tokens · 40424 ms · 2026-05-20T16:09:46.151669+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

68 extracted references · 68 canonical work pages

  1. [1]

    Li.Advanced control theory for practical applications

    H. Li.Advanced control theory for practical applications. Shanghai Jiao Tong University Press, 2026. [2]李颢.面向实际应用的高级控制理论（英文版）.上海交通大学出版社, 2026

    work page 2026

  2. [2]

    H. Li, F. Nashashibi, and G. Toulminet. Localization for intelligent vehicle by fus- ing mono-camera, low-cost GPS and map data. InIEEE International Conference on Intelligent Transportation Systems, pages 1657–1662, 2010

    work page 2010

  3. [3]

    Li and F

    H. Li and F. Nashashibi. Multi-vehicle cooperative perception and augmented reality for driver assistance: A possibility to see through front vehicle. InIEEE International Conference on Intelligent Transportation Systems, pages 242–247, 2011

    work page 2011

  4. [4]

    H. Li, M. Tsukada, F. Nashashibi, and M. Parent. Multivehicle cooperative local map- ping: a methodology based on occupancy grid map merging.IEEE Transactions on Intelligent Transportation Systems, 15(5):2089 – 2100, 2014

    work page 2089

  5. [5]

    Ying and H

    Z. Ying and H. Li. IMM-SLAMMOT: tightly-coupled SLAM and IMM-based multi- object tracking.IEEE Transactions on Intelligent Vehicles, 9(2):3964–3974, 2024

    work page 2024

  6. [6]

    Fang and H

    S. Fang and H. Li. Multi-vehicle cooperative simultaneous lidar SLAM and object tracking in dynamic environments.IEEE Transactions on Intelligent Transportation Systems, 25(9):11411–11421, 2024

    work page 2024

  7. [7]

    Chen and H

    C. Chen and H. Li. Robust representation learning with feedback for single image deraining. InIEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7742–7751, 2021

    work page 2021

  8. [8]

    Li.Control theory for practical applications: with MATLAB demonstration programs

    H. Li.Control theory for practical applications: with MATLAB demonstration programs. Springer, 2024. [10]李颢.面向实际应用的控制理论（英文版）.上海交通大学出版社, 2024

    work page 2024

  9. [9]

    Fraden.Handbook of modern sensors: physics, designs, and applications

    J. Fraden.Handbook of modern sensors: physics, designs, and applications. Springer Science & Business Media, 2010

    work page 2010

  10. [10]

    Li.Geometry fundamentals of computer vision

    H. Li.Geometry fundamentals of computer vision. Shanghai Jiao Tong University Press, 2025

    work page 2025

  11. [11]

    R. Kalman. A new approach to linear filtering and prediction problem.ASME Trans, Ser. D, J. Basic Eng., 82:35–45, 1960

    work page 1960

  12. [12]

    Li.Fundamentals and applications of recursive estimation theory

    H. Li.Fundamentals and applications of recursive estimation theory. Shanghai Jiao Tong University Press, 2022. [15]李颢.迭代估计理论基础与应用（英文版）.上海交通大学出版社, 2022

    work page 2022

  13. [13]

    Murphy.Dynamic Bayesian networks: Representation, inference and learning

    K. Murphy.Dynamic Bayesian networks: Representation, inference and learning. Ph.D. Dissertation, UC Berkeley, 2002

    work page 2002

  14. [14]

    Horn and C

    R. Horn and C. Johnson.Matrix analysis. Cambridge University Press, 2012

    work page 2012

  15. [15]

    Golub and C

    G. Golub and C. Van Loan.Matrix computations. Johns Hopkins University Press, 1996

    work page 1996

  16. [16]

    P. Kalata. The tracking index: A generalized parameter forα-βandα-β-γtarget trackers.IEEE Transactions on Aerospace and Electronic Systems, 20(2):174–182, 1984

    work page 1984

  17. [17]

    Durrett.Probability: theory and examples

    R. Durrett.Probability: theory and examples. Cambridge university press, 2019

    work page 2019

  18. [18]

    Mitrinovic and P

    D. Mitrinovic and P. Vasic.Analytic inequalities. Springer-Verlag Berlin Heidelberg, 1970

    work page 1970

  19. [19]

    Li and V

    X. Li and V. Jilkov. Survey of maneuvering target tracking. part i: Dynamic models. IEEE Transactions on Aerospace and Electronic Systems, 39(4):1333–1364, 2003

    work page 2003

  20. [20]

    Li and V

    X. Li and V. Jilkov. Survey of maneuvering target tracking. part ii: Motion models of ballistic and space targets.IEEE Transactions on Aerospace and Electronic Systems, 46(1):96–119, 2010

    work page 2010

  21. [21]

    Li and V

    X. Li and V. Jilkov. Survey of maneuvering target tracking. part iii: Measurement models. InSPIE Conference on Signal and Data Processing of Small Targets, page 423—446, 2001

    work page 2001

  22. [22]

    Li and V

    X. Li and V. Jilkov. Survey of maneuvering target tracking. part iv: Decision-based methods. InSPIE Conference on Signal and Data Processing of Small Targets, page 511—534, 2002

    work page 2002

  23. [23]

    Li and V

    X. Li and V. Jilkov. Survey of maneuvering target tracking. part v: Multiple-model methods.IEEE Transactions on Aerospace and Electronic Systems, 41(4):1255—1321, 2005

    work page 2005

  24. [24]

    Blom and Y

    H. Blom and Y. Bar-Shalom. The interacting multiple model algorithm for systems with markovian switching coefficients.IEEE Transactions on Automatic Control, 33(8):780– 783, 1988

    work page 1988

  25. [25]

    Mazor, A

    E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan. Interacting multiple model methods in target tracking: a survey.IEEE Transactions on Aerospace and Electronic Systems, 34(1):103–123, 1998

    work page 1998

  26. [26]

    Grewal and A

    M. Grewal and A. Andrews.Kalman filtering: Theory and practice. New York, USA: Wiley, 2000

    work page 2000

  27. [27]

    Julier and J

    S. Julier and J. Uhlmann. A new extension of the kalman filter to nonlinear systems. Signal Processing, Sensor Fusion, and Target Recognition, 3068:182–193, 1997

    work page 1997

  28. [28]

    Julier and J.K

    S.J. Julier and J.K. Uhlmann. Unscented filtering and nonlinear estimation.Proceedings of the IEEE, 92(3):401–422, 2004

    work page 2004

  29. [29]

    Uhlmann.Dynamic map building and localization: New theoretical foundations

    J.K. Uhlmann.Dynamic map building and localization: New theoretical foundations. Ph.D. Dissertation, University of Oxford, 1995

    work page 1995

  30. [30]

    Arasaratnam and S

    I. Arasaratnam and S. Haykin. Cubature kalman filters.IEEE Transactions on Auto- matic Control, 54(6):1254–1269, 2009

    work page 2009

  31. [31]

    Sorenson and D

    H. Sorenson and D. Alspach. Recursive bayesian estimation using gaussian sums.Au- tomatica, 7(4):465–479, 1971

    work page 1971

  32. [32]

    Alspach and H

    D. Alspach and H. Sorenson. Nonlinear bayesian estimation using gaussian sum approx- imations.IEEE Transactions on Automatic Control, 17(4):439–448, 1972

    work page 1972

  33. [33]

    Doucet, N

    A. Doucet, N. De Freitas, and N. Gordon.Sequential Monte Carlo methods in practice. New York, USA: Springer-Verlag, 2001

    work page 2001

  34. [34]

    Arulampalam, S

    M.S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A tutorial on particle fil- ters for online nonlinear/non-gaussian bayesian tracking.IEEE Transactions on Signal Processing, 50(2):174–188, 2002

    work page 2002

  35. [35]

    Julier and J

    S. Julier and J. Uhlmann. A non-divergent estimation algorithm in the presence of unknown correlations. InProceedings of American Control Conference, pages 2369– 2373, 1997

    work page 1997

  36. [36]

    Julier and J

    S. Julier and J. Uhlmann. General decentralized data fusion with covariance intersection (ci).Handbook of Data Fusion, 2001

    work page 2001

  37. [37]

    H. Li, F. Nashashibi, and M. Yang. Split covariance intersection filter: Theory and its application to vehicle localization.IEEE Transactions on Intelligent Transportation Systems, 14(4):1860–1871, 2013

    work page 2013

  38. [38]

    D. Fox, W. Burgard, H. Kruppa, and S. Thrun. A probabilistic approach to collaborative multi-robot localization.Autonomous robots, 8(3):325–344, 2000

    work page 2000

  39. [39]

    A. Howard. Multi-robot simultaneous localization and mapping using particle filters. The International Journal of Robotics Research, 25(12):1243–1256, 2006

    work page 2006

  40. [40]

    Li and F

    H. Li and F. Nashashibi. Cooperative multi-vehicle localization using split covariance intersection filter.IEEE Intelligent Transportation Systems Magazine, 5(2):33–44, 2013

    work page 2013

  41. [41]

    Bar-Shalom and L

    Y. Bar-Shalom and L. Campo. The effect of the common process noise on the two- sensor fused-track covariance.IEEE Transactions on Aerospace and Electronic Systems, 22(6):803–805, 1986

    work page 1986

  42. [42]

    Chang, R

    K. Chang, R. Saha, and Y. Bar-Shalom. On optimal track-to-track fusion.IEEE Trans- actions on Aerospace and Electronic Systems, 33(4):1271–1276, 1997

    work page 1997

  43. [43]

    N. Carlson. Federated square root filter for decentralized parallel processors.IEEE Transactions on Aerospace and Electronic Systems, 26(3):517–525, 1990

    work page 1990

  44. [44]

    Pierre, R

    C. Pierre, R. Chapuis, R. Aufr` ere, J. Laneurit, and C. Debain. Range-only based co- operative localization for mobile robots. InInternational Conference on Information Fusion, pages 1933–1939, 2018

    work page 1933

  45. [45]

    X. Chen, M. Yang, W. Yuan, H. Li, and C. Wang. Split covariance intersection filter based front-vehicle track estimation for vehicle platooning without communication. In IEEE Intelligent Vehicles Symposium, pages 1510–1515, 2020

    work page 2020

  46. [46]

    S. Fang, H. Li, and M. Yang. Lidar slam based multivehicle cooperative localization using iterated Split CIF.IEEE Transactions on Intelligent Transportation Systems, 23(11):21137–21147, 2022

    work page 2022

  47. [47]

    H. Li, F. Nashashibi, B. Lefaudeux, and E. Pollard. Track-to-track fusion using split co- variance intersection filter-information matrix filter (SCIF-IMF) for vehicle surrounding environment perception. InIEEE International Conference on Intelligent Transportation Systems, pages 1430–1435, 2013

    work page 2013

  48. [48]

    S. Fang, H. Li, M. Yang, and Z. Wang. Inertial navigation system based vehicle tem- poral relative localization with split covariance intersection filter.IEEE Robotics and Automation Letters, 7(2):5270–5277, 2022

    work page 2022

  49. [49]

    S. Fang, Y. Li, and H. Li. Split covariance intersection filter based visual localization with accurate apriltag map for warehouse robot navigation.arXiv, 2023

    work page 2023

  50. [50]

    H. Li, B. Liu, and L. Wang. Vehicle top tag assisted vehicle-road cooperative localization for autonomous public buses.arXiv, 2025

    work page 2025

  51. [51]

    Bar-Shalom and E

    Y. Bar-Shalom and E. Tse. Tracking in a cluttered environment with probabilistic data association.Automatica, 11(5):451–460, 1975

    work page 1975

  52. [52]

    Blackman.Multiple-target tracking with radar applications

    S. Blackman.Multiple-target tracking with radar applications. Norwood Artech House, 1986

    work page 1986

  53. [53]

    Z. Wang, J. Zhan, C. Duan, X. Guan, P. Lu, and K. Yang. A review of vehicle detection techniques for intelligent vehicles.IEEE Transactions on Neural Networks and Learning Systems, 34(8):3811–3831, 2023

    work page 2023

  54. [54]

    Y. Yuan, Y. Lu, and Q. Wang. Tracking as a whole: multi-target tracking by modeling group behavior with sequential detection.IEEE Transactions on Intelligent Transporta- tion Systems, 18(12):3339–3349, 2017

    work page 2017

  55. [55]

    Blackman

    S. Blackman. Multiple hypothesis tracking for multiple target tracking.IEEE Aerospace and Electronic Systems Magazine, 19(1):5–18, 2004

    work page 2004

  56. [56]

    R. Mahler. Multitarget bayes filtering via first-order multitarget moments.IEEE Trans- actions on Aerospace and Electronic Systems, 39(4):1152–1178, 2003. References 81

    work page 2003

  57. [57]

    R. Bellman. The theory of dynamic programming.Bulletin of the American Mathemat- ical Society, 60(6):503–515, 1954

    work page 1954

  58. [58]

    Vo and W

    B. Vo and W. Ma. The Gaussian mixture probability hypothesis density filter.IEEE Transactions on Signal Processing, 54(11):4091–4104, 2006

    work page 2006

  59. [59]

    B. Vo, B. Vo, and A. Cantoni. The cardinalized probability hypothesis density filter for linear Gaussian multi-target models. InAnnual Conference on Information Sciences and Systems, pages 681–686, 2006

    work page 2006

  60. [60]

    Fukunaga and L

    K. Fukunaga and L. Hostetler. The estimation of the gradient of a density function, with applications in pattern recognition.IEEE Transactions on Information Theory, 21(1):32–40, 1975

    work page 1975

  61. [61]

    Y. Cheng. Mean shift, mode seeking, and clustering.IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8):790–799, 1995

    work page 1995

  62. [62]

    Comaniciu and P

    D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603–619, 2002

    work page 2002

  63. [63]

    Mahalanobis

    P. Mahalanobis. On the generalized distance in statistics.Proceedings of the National Institute of Sciences of India, 2(1):49–55, 1936

    work page 1936

  64. [64]

    Bucy and P

    R. Bucy and P. Joseph.Filtering for stochastic processes with applications to guidance. American Mathematical Society, 2005

    work page 2005

  65. [65]

    Nguyen and Z

    T. Nguyen and Z. Gajic. Solving the matrix differential Riccati equation: A Lyapunov equation approach.IEEE Transactions on Automatic Control, 55(1):191–194, 2010

    work page 2010

  66. [66]

    Agarwal and J

    A. Agarwal and J. Lang.Foundations of analog and digital electronic circuits. Elsevier, 2005

    work page 2005

  67. [67]

    Asadi.Analog electronic circuits laboratory manual

    F. Asadi.Analog electronic circuits laboratory manual. Springer, 2023

    work page 2023

  68. [68]

    Luenberger

    D. Luenberger. Observing the state of a linear system.IEEE Transactions on Military Electronics, 8(2):74–80, 1964

    work page 1964