The Kalman filter based on singular value decomposition (SVD) can sufficiently reduce the accumulation of rounding errors and is widely used in various applications with numerical calculations. However, in order to improve the filtering performance and adaptability in a tightly GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation system, we propose an improved robust method to satisfy the requirements. To solve the issue of large fluctuations in GNSS signals faced by the conventional method that uses a fixed noise covariance, the proposed method constructs a correction variable through the innovation and the new matrix which is obtained by performing SVD on the original matrix, dynamically correcting the noise covariance and has better robustness. In addition, the derived SVD form of the information filter (IF) extends its application. The proposed method has higher positioning accuracy and can be better applied to tightly coupled GNSS/INS navigation simulations and physical experiments. The experimental results show that, compared with the traditional Kalman algorithm based on SVD, the proposed algorithm*s maximum error is reduced by 45.77%. Compared with the traditional IF algorithm, the root mean squared error of the proposed IF algorithm in the form of SVD is also reduced by 4.7%.
Citation: Yuelin Yuan, Fei Li, Jialiang Chen, Yu Wang, Kai Liu. An improved Kalman filter algorithm for tightly GNSS/INS integrated navigation system[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 963-983. doi: 10.3934/mbe.2024040
[1] | Eunha Shim . Optimal strategies of social distancing and vaccination against seasonal influenza. Mathematical Biosciences and Engineering, 2013, 10(5&6): 1615-1634. doi: 10.3934/mbe.2013.10.1615 |
[2] | Hamed Karami, Pejman Sanaei, Alexandra Smirnova . Balancing mitigation strategies for viral outbreaks. Mathematical Biosciences and Engineering, 2024, 21(12): 7650-7687. doi: 10.3934/mbe.2024337 |
[3] | Pannathon Kreabkhontho, Watchara Teparos, Thitiya Theparod . Potential for eliminating COVID-19 in Thailand through third-dose vaccination: A modeling approach. Mathematical Biosciences and Engineering, 2024, 21(8): 6807-6828. doi: 10.3934/mbe.2024298 |
[4] | Sarafa A. Iyaniwura, Musa Rabiu, Jummy F. David, Jude D. Kong . Assessing the impact of adherence to Non-pharmaceutical interventions and indirect transmission on the dynamics of COVID-19: a mathematical modelling study. Mathematical Biosciences and Engineering, 2021, 18(6): 8905-8932. doi: 10.3934/mbe.2021439 |
[5] | Lili Liu, Xi Wang, Yazhi Li . Mathematical analysis and optimal control of an epidemic model with vaccination and different infectivity. Mathematical Biosciences and Engineering, 2023, 20(12): 20914-20938. doi: 10.3934/mbe.2023925 |
[6] | Antonios Armaou, Bryce Katch, Lucia Russo, Constantinos Siettos . Designing social distancing policies for the COVID-19 pandemic: A probabilistic model predictive control approach. Mathematical Biosciences and Engineering, 2022, 19(9): 8804-8832. doi: 10.3934/mbe.2022409 |
[7] | Seyedeh Nazanin Khatami, Chaitra Gopalappa . Deep reinforcement learning framework for controlling infectious disease outbreaks in the context of multi-jurisdictions. Mathematical Biosciences and Engineering, 2023, 20(8): 14306-14326. doi: 10.3934/mbe.2023640 |
[8] | Avinash Shankaranarayanan, Hsiu-Chuan Wei . Mathematical modeling of SARS-nCoV-2 virus in Tamil Nadu, South India. Mathematical Biosciences and Engineering, 2022, 19(11): 11324-11344. doi: 10.3934/mbe.2022527 |
[9] | Chloe Bracis, Mia Moore, David A. Swan, Laura Matrajt, Larissa Anderson, Daniel B. Reeves, Eileen Burns, Joshua T. Schiffer, Dobromir Dimitrov . Improving vaccination coverage and offering vaccine to all school-age children allowed uninterrupted in-person schooling in King County, WA: Modeling analysis. Mathematical Biosciences and Engineering, 2022, 19(6): 5699-5716. doi: 10.3934/mbe.2022266 |
[10] | Amira Bouhali, Walid Ben Aribi, Slimane Ben Miled, Amira Kebir . Impact of immunity loss on the optimal vaccination strategy for an age-structured epidemiological model. Mathematical Biosciences and Engineering, 2024, 21(6): 6372-6392. doi: 10.3934/mbe.2024278 |
The Kalman filter based on singular value decomposition (SVD) can sufficiently reduce the accumulation of rounding errors and is widely used in various applications with numerical calculations. However, in order to improve the filtering performance and adaptability in a tightly GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation system, we propose an improved robust method to satisfy the requirements. To solve the issue of large fluctuations in GNSS signals faced by the conventional method that uses a fixed noise covariance, the proposed method constructs a correction variable through the innovation and the new matrix which is obtained by performing SVD on the original matrix, dynamically correcting the noise covariance and has better robustness. In addition, the derived SVD form of the information filter (IF) extends its application. The proposed method has higher positioning accuracy and can be better applied to tightly coupled GNSS/INS navigation simulations and physical experiments. The experimental results show that, compared with the traditional Kalman algorithm based on SVD, the proposed algorithm*s maximum error is reduced by 45.77%. Compared with the traditional IF algorithm, the root mean squared error of the proposed IF algorithm in the form of SVD is also reduced by 4.7%.
Let
● (Divisorial)
● (Flipping)
● (Mixed)
Note that the mixed case can occur only if either
We can almost always choose the initial
Our aim is to discuss a significant special case where the
Definition 1 (MMP with scaling). Let
By the
(Xj,Θj)ϕj→Zjψj←(Xj+1,Θj+1)gj↘↓↙gj+1S | (1.1) |
where
(2)
(3)
(4)
Note that (4) implies that
In general such a diagram need not exist, but if it does, it is unique and then
(X,Θ)ϕ→Zϕ+←(X+,Θ+)g↘↓↙g+S | (1.5) |
We say that the MMP terminates with
(6) either
(7) or
Warning 1.8. Our terminology is slightly different from [7], where it is assumed that
One advantage is that our MMP steps are uniquely determined by the starting data. This makes it possible to extend the theory to algebraic spaces [33].
Theorem 2 is formulated for Noetherian base schemes. We do not prove any new results about the existence of flips, but Theorem 2 says that if the MMP with scaling exists and terminates, then its steps are simpler than expected, and the end result is more controlled than expected.
On the other hand, for 3-dimensional schemes, Theorem 2 can be used to conclude that, in some important cases, the MMP runs and terminates, see Theorem 9.
Theorem 2. Let
(i)
(ii)
(iii)
(iv)
(v) The
We run the
(1)
(a) either
(b) or
(2) The
(3)
Furthermore, if the MMP terminates with
(4)
(5) if
Remark 2.6. In applications the following are the key points:
(a) We avoided the mixed case.
(b) In the fipping case we have both
(c) In (3) we have an explicit, relatively ample, exceptional
(d) In case (5) we end with
(e) In case (5) the last MMP step is a divisorial contraction, giving what [35] calls a Kollár component; no further flips needed.
Proof. Assertions (1-3) concern only one MMP-step, so we may as well drop the index
Let
∑hi(Ei⋅C)=−r−1(EΘ⋅C). | (2.7) |
By Lemma 3 this shows that the
∑hi(e′(Ei⋅C)−e(Ei⋅C′))=0. | (2.8) |
By the linear independence of the
Assume first that
ϕ∗(EΘ+rH)=∑i>1(ei+rhi)ϕ∗(Ei) |
is
Otherwise
g−1(g(supp(EΘ+rH)))=supp(EΘ+rH). | (2.9) |
If
Thus
Assume next that the flip
Finally, if the MMP terminates with
Lemma 3. Let
∑ni=1hivi=γv0 for some γ∈L. |
Then
Proof. We may assume that
∑ni=1hiai=γa0 and n∑i=1hibi=γb0. |
This gives that
∑ni=1hi(b0ai−a0bi)=0. |
Since the
Lemma 4. Let
Proof. Assume that
∑ni=1sihi=−(∑ni=1siei)⋅∑ni=0rihi. |
If
The following is a slight modifications of [3,Lem.1.5.1]; see also [17,5.3].
Lemma 5. Let
Comments on
Conjecture 6. Let
(1)
(2) The completion of
Using [30,Tag 0CAV] one can reformulate (6.2) as a finite type statement:
(3) There are elementary étale morphisms
(x,X,∑DXi)←(u,U,∑DUi)→(y,Y,∑DYi). |
Almost all resolution methods commute with étale morphisms, thus if we want to prove something about a resolution of
A positive answer to Conjecture 6 (for
(Note that [27] uses an even stronger formulation: Every normal, analytic singularity has an algebraization whose class group is generated by the canonical class. This is, however, not true, since not every normal, analytic singularity has an algebraization.)
Existence of certain resolutions.
7 (The assumptions 2.i-v). In most applications of Theorem 2 we start with a normal pair
Typically we choose a log resolution
We want
The existence of a
8 (Ample, exceptional divisors). Assume that we blow up an ideal sheaf
Claim 8.1. Let
Resolution of singularities is also known for 3-dimensional excellent schemes [10], but in its original form it does not guarantee projectivity in general. Nonetheless, combining [6,2.7] and [23,Cor.3] we get the following.
Claim 8.2. Let
Next we mention some applications. In each case we use Theorem 2 to modify the previous proofs to get more general results. We give only some hints as to how this is done, we refer to the original papers for definitions and details of proofs.
The first two applications are to dlt 3-folds. In both cases Theorem 2 allows us to run MMP in a way that works in every characteristic and also for bases that are not
Relative MMP for dlt 3-folds.
Theorem 9. Let
Then the MMP over
(1) each step
(a) either a contraction
(b) or a flip
(2)
(3) if either
Proof. Assume first that the MMP steps exist and the MMP terminates. Note that
KX+E+g−1∗Δ∼Rg∗(KY+Δ)+∑j(1+a(Ej,Y,Δ))Ej∼g,R∑j(1+a(Ej,Y,Δ))Ej=:EΘ. |
We get from Theorem 2 that (1.a-b) are the possible MMP-steps, and (2-3) from Theorem 15-5.
For existence and termination, all details are given in [6,9.12].
However, I would like to note that we are in a special situation, which can be treated with the methods that are in [1,29], at least when the closed points of
The key point is that everything happens inside
Contractions for reducible surfaces have been treated in [1,Secs.11-12], see also [12,Chap.6] and [31].
The presence of
The short note [34] explains how [15,3.4] gives 1-complemented 3-fold flips; see [16,3.1 and 4.3] for stronger results.
Inversion of adjunction for 3-folds. Using Theorem 9 we can remove the
Corollary 10. Let
This implies that one direction of Reid's classification of terminal singularities using 'general elephants' [28,p.393] works in every characteristic. This could be useful in extending [2] to characteristics
Corollary 11. Let
Divisor class group of dlt singularities. The divisor class group of a rational surface singularity is finite by [24], and [8] plus an easy argument shows that the divisor class group of a rational 3-dimensional singularity is finitely generated. Thus the divisor class group of a 3-dimensional dlt singularity is finitely generated in characteristic
Proposition 12. [21,B.1] Let
It seems reasonable to conjecture that the same holds in all dimensions, see [21,B.6].
Grauert-Riemenschneider vanishing. One can prove a variant of the Grauert-Riemenschneider (abbreviated as G-R) vanishing theorem [13] by following the steps of the MMP.
Definition 13 (G-R vanishing). Let
Let
(1)
(2)
Then
We say that G-R vanishing holds over
By an elementary computation, if
If
G-R vanishing also holds over 2-dimensional, excellent schemes by [24]; see [20,10.4]. In particular, if
However, G-R vanishing fails for 3-folds in every positive characteristic, as shown by cones over surfaces for which Kodaira's vanishing fails. Thus the following may be the type of G-R vanishing result that one can hope for.
Theorem 14. [5] Let
Proof. Let
A technical problem is that we seem to need various rationality properties of the singularities of the
For divisorial contractions
For flips
From G-R vanishing one can derive various rationality properties for all excellent dlt pairs. This can be done by following the method of 2 spectral sequences as in [19] or [20,7.27]; see [5] for an improved version.
Theorem 15. [5] Let
(1)
(2) Every irreducible component of
(3) Let
See [5,12] for the precise resolution assumptions needed. The conclusions are well known in characteristic 0, see [22,5.25], [12,Sec.3.13] and [20,7.27]. For 3-dimensional dlt varieties in
The next two applications are in characteristic 0.
Dual complex of a resolution. Our results can be used to remove the
Corollary 16. Let
Theorem 17. Let
(1)
(2)
(3)
Then
Proof. Fix
Let us now run the
Note that
We claim that each MMP-step as in Theorem 2 induces either a collapse or an isomorphism of
By [11,Thm.19] we get an elementary collapse (or an isomorphism) if there is a divisor
It remains to deal with the case when we contract
Dlt modifications of algebraic spaces. By [25], a normal, quasi-projective pair
However, dlt modifications are rarely unique, thus it was not obvious that they exist when the base is not quasi-projective. [33] observed that Theorem 2 gives enough uniqueness to allow for gluing. This is not hard when
Theorem 18 (Villalobos-Paz). Let
(1)
(2)
(3)
(4)
(5) either
I thank E. Arvidsson, F. Bernasconi, J. Carvajal-Rojas, J. Lacini, A. Stäbler, D. Villalobos-Paz, C. Xu for helpful comments and J. Witaszek for numerous e-mails about flips.
[1] | Q. Wang, X. Hu, An improve differential algorithm for GPS static positioning, in 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, (2008), 58–61. https://doi.org/10.1109/KAMW.2008.4810424 |
[2] | M. Shao, X. Sui, Study on differential GPS positioning methods, in 2015 International Conference on Computer Science and Mechanical Automation (CSMA), (2015), 223–225. https://doi.org/10.1109/CSMA.2015.51 |
[3] | X. Gan, B. Yu, Research on multimodal SBAS technology supporting precision single point positioning, in 2015 International Conference on Computers, Communications, and Systems (ICCCS), (2015), 131–135. https://doi.org/10.1109/CCOMS.2015.7562887 |
[4] |
Y. Xu, K. Wang, C. Yang, Z. Li, F. Zhou, D. Liu, GNSS/INS/OD/NHC Adaptive Integrated Navigation Method Considering the Vehicle Motion State, IEEE Sensors J., 23 (2023), 13511–13523. https://doi.org/10.1109/JSEN.2023.3272507 doi: 10.1109/JSEN.2023.3272507
![]() |
[5] | G. Chen, J. Wang, H. Hu, An integrated GNSS/INS/DR positioning strategy considering nonholonomic constraints for intelligent vehicle, in 2022 6th CAA International Conference on Vehicular Control and Intelligence (CVCI), (2022), 1–6. https://doi.org/10.1109/CVCI56766.2022.9964661 |
[6] | G. Wan, X. Yang, R. Cai, H. Li, Y. Zhou, H. Wang, et al., Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes, in 2018 IEEE International Conference on Robotics and Automation (ICRA), (2018), 4670–4677. https://doi.org/10.1109/ICRA.2018.8461224 |
[7] |
S. Liu, K. Wang, D. Abel, Robust state and protection-level estimation within tightly coupled GNSS/INS navigation system, GPS Solut., 27 (2023). https://doi.org/10.1007/s10291-023-01447-z doi: 10.1007/s10291-023-01447-z
![]() |
[8] |
Z. Gao, M. Ge, Y. Li, Y. Pan, Q. Chen, H. Zhang, Modeling of multisensor tightly aided BDS triple-frequency precise point positioning and initial assessments, Inform. Fusion, 55 (2020), 184–198. https://doi.org/10.1016/j.inffus.2019.08.012 doi: 10.1016/j.inffus.2019.08.012
![]() |
[9] |
T. Xu, Adaptive Kalman Filter for INS/GPS integrated navigation system, Appl. Mechan. Mater., (2013), 332–335. https://doi.org/10.4028/www.scientific.net/AMM.336-338.332 doi: 10.4028/www.scientific.net/AMM.336-338.332
![]() |
[10] |
X. Feng, T. Zhang, T Lin, H. Tang, X. Niu, Implementation and performance of a deeply coupled GNSS receiver with low-cost MEMS inertial sensors for vehicle urban navigation, Sensors (Basel), 20 (2020). https://doi.org/10.3390/s20123397 doi: 10.3390/s20123397
![]() |
[11] |
B. Liu, X. Zhan, M. Liu, GNSS/MEMS IMU ultra-tightly integrated navigation system based on dual-loop NCO control method and cascaded channel filters, IET Radar Sonar Navigat., 12 (2018), 1241–1250. https://doi.org/10.1049/iet-rsn.2018.5169 doi: 10.1049/iet-rsn.2018.5169
![]() |
[12] | J. Yu, X. Chen, Application of extended Kalman filter in ultra-tight GPS/INS integration based on GPS software receiver, in The 2010 International Conference on Green Circuits and Systems, (2010), 82–86. https://doi.org/10.1109/ICGCS.2010.5543092 |
[13] |
F. R. Kschischang, B. J. Frey, H. A. Loeliger, Factor graphs and the sum-product algorithm, IEEE Transact. Inform. Theory, 47 (2001), 498–519. https://doi.org/10.1109/18.910572 doi: 10.1109/18.910572
![]() |
[14] |
M. Kaess, A. Ranganathan, F. Dellaert, iSAM: Incremental smoothing and mapping, IEEE Transact. Robot., 24 (2008), 1365–1378. https://doi.org/10.1109/TRO.2008.2006706 doi: 10.1109/TRO.2008.2006706
![]() |
[15] |
M. Kaess, H. Johannsson, R. Roberts, V. Ila, J. J. Leonard, F. Dellaert, iSAM2: Incremental smoothing and mapping using the Bayes tree, Int. J. Robot. Res., 31 (2012), 216–235. https://doi.org/10.1177/0278364911430419 doi: 10.1177/0278364911430419
![]() |
[16] |
J. Wahlstrom, I. Skog, Fifteen Years of Progress at Zero Velocity: A Review, IEEE Sensors J., 21 (2021), 1139–1151. https://doi.org/10.1109/JSEN.2020.3018880 doi: 10.1109/JSEN.2020.3018880
![]() |
[17] |
T. Zhao, M. J. Ahamed, Pseudo-zero velocity re-detection double threshold zero-velocity update (ZUPT) for inertial sensor-based pedestrian navigation, IEEE Sensors J., 21 (2021), 13772–13785. https://doi.org/10.1109/JSEN.2021.3070144 doi: 10.1109/JSEN.2021.3070144
![]() |
[18] |
I. Skog, P. Handel, J. Nilsson, J. Rantakokko, Zero-Velocity detection—An algorithm evaluation, IEEE Transact. Biomed. Eng., 57 (2010), 2657–2666. https://doi.org/10.1109/TBME.2010.2060723 doi: 10.1109/TBME.2010.2060723
![]() |
[19] | H. Lan, Y. Sarvrood, A. Moussa, N. El-Sheimy, Zero velocity detection for un-tethered vehicular navigation systems using support vector machine, in 32nd International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2019). (2019), 54–61. https://doi.org/10.33012/2020.17652 |
[20] |
H. Lau, K. Tong, H. Zhu, Support vector machine for classification of walking conditions using miniature kinematic sensors, Med. Biol. Eng. Comput., 46 (2008), 563–573. https://doi.org/10.1007/s11517-008-0327-x doi: 10.1007/s11517-008-0327-x
![]() |
[21] | X. Yu, B. Liu, X. Lan Z. Xiao, S. Lin, B. Yan et al, AZUPT: Adaptive Zero Velocity Update based on neural networks for pedestrian tracking, in IEEE Global Communications Conference (GLOBECOM), (2019), 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9014070 |
[22] | B. Wagstaff, J. Kelly, LSTM-based zero-velocity detection for robust inertial navigation, in 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), (2018), 1–8. |
[23] |
B. Wagstaff, V. Peretroukhin, J. Kelly, Robust Data-Driven Zero-Velocity Detection for Foot-Mounted Inertial Navigation, IEEE Sensors J., 20 (2019), 957–967. https://doi.org/10.1109/JSEN.2019.2944412 doi: 10.1109/JSEN.2019.2944412
![]() |
[24] | L. Wang, G. Libert, P. Minneback, A singular value decomposition based Kalman filter algorithm, in Proceedings of the 1992 International Conference on Industrial Electronics, 3 (1992), 1352–1357. https://doi.org/10.1109/IECON.1992.254406 |
[25] |
M. V. Kulikova, J. V. Tsyganova, Improved discrete-time Kalman filtering within singular value decomposition, IET Control Theory Appl., 11 (2017), 2412–2418. https://doi.org/10.1049/iet-cta.2016.1282 doi: 10.1049/iet-cta.2016.1282
![]() |
[26] |
R. Mehra. Approaches to adaptive filtering, IEEE Transact. Autom. Control, 17 (1972), 693–698. https://doi.org/10.1109/TAC.1972.1100100 doi: 10.1109/TAC.1972.1100100
![]() |
[27] |
A. Mohamed, K. Schwarz, Adaptive Kalman Filtering for INS/GPS, J. Geodesy, 73 (1999), 193–203. https://doi.org/10.1007/s001900050236 doi: 10.1007/s001900050236
![]() |
[28] | A. Fakharian, T. Gustafsson, M. Mehrfam, Adaptive Kalman filtering based navigation: An IMU/GPS integration approach, in 2011 International Conference on Networking, (2011), 181–185. https://doi.org/10.1109/ICNSC.2011.5874871 |
[29] |
A. Werries, J. Dolan, Adaptive Kalman Filtering methods for Low-Cost GPS/INS localization for autonomous vehicles, Carnegie Mellon University, (2018). https://doi.org/10.1184/R1/6551687.v1 doi: 10.1184/R1/6551687.v1
![]() |
[30] | Y. Luo, G. Ye, Y. Wu, J. Guo, J. Liang, Y. Yang, An adaptive Kalman Filter for UAV attitude estimation, in 2019 IEEE 2nd International Conference on Electronics Technology (ICET), (2019). https://doi.org/10.1109/ELTECH.2019.8839496 |
[31] |
J. Bermudez, R. Valdés, V. Comendador, Engineering applications of adaptive Kalman Filtering based on singular value decomposition (SVD), Appl. Sci., 10 (2020). https://doi.org/10.3390/app10155168 doi: 10.3390/app10155168
![]() |
[32] |
Y. Liu, X. Fan, L. Chen, An innovative information fusion method with adaptive Kalman filter for integrated INS/GPS navigation of autonomous vehicles, Mechan. Syst. Signal Process., 100 (2017), 605–616. https://doi.org/10.1016/j.ymssp.2017.07.051 doi: 10.1016/j.ymssp.2017.07.051
![]() |
[33] |
C. Pan, N. Qian, Z. Li, J. Gao, Z. Liu, K. Shao, A Robust Adaptive Cubature Kalman Filter Based on SVD for Dual-Antenna GNSS/MIMU Tightly Coupled Integration, Remote Sens., 13 (2021). https://doi.org/10.3390/rs13101943 doi: 10.3390/rs13101943
![]() |
[34] |
Y. Yang, T. Xu, An adaptive Kalman Filter based on sage windowing weights and variance components, J. Navigat., 56 (2003), 231–240. https://doi.org/10.1017/S0373463303002248 doi: 10.1017/S0373463303002248
![]() |
[35] | I. Vitanov, N. Aouf, Fault diagnosis and recovery in MEMS inertial navigation system using information filters and Gaussian processes, in 22nd Mediterranean Conference on Control and Automation, (2014), 115–120. https://doi.org/10.1109/MED.2014.6961357 |
[36] | P. D. Groves, INS/GNSS integration, in Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems 2nd Edition (eds. Paul D. Groves), Artech House, (2013), 602–606. https://doi.org/10.1109/MAES.2014.14110 |
1. | Balázs Csutak, Gábor Szederkényi, Robust control and data reconstruction for nonlinear epidemiological models using feedback linearization and state estimation, 2025, 22, 1551-0018, 109, 10.3934/mbe.2025006 |