Rework introduction and cleaned BIB file

2021-08-27 11:27:59 +02:00
parent b6ff1da85d
commit bc3efd689d
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--- a/journal/dehaeze21_desig_compl_filte.org
+++ b/journal/dehaeze21_desig_compl_filte.org
@@ -81,46 +81,67 @@ Sensor fusion \sep{} Optimal filters \sep{} $\mathcal{H}_\infty$ synthesis \sep{

 * Introduction
 <<sec:introduction>>
-** New introduction                                                  :ignore:
-*** Introduction to Sensor Fusion                                   :ignore:
+** Introduction to Sensor Fusion                                     :ignore:

 # Basic explanations of sensor fusion
+# "Fusing" several sensor

+- cite:anderson53_instr_approac_system_steer_comput earliest application of complementary filters (A simple RC circuit was used to physically realize the complementary filters)
 - cite:bendat57_optim_filter_indep_measur_two roots of sensor fusion

-*** Advantages of Sensor Fusion                                     :ignore:
+** Advantages of Sensor Fusion                                       :ignore:

 # Sensor Fusion can have many advantages / can be applied for various purposes
+Sensor fusion can have many advantages.

- Increase the bandwidth: cite:zimmermann92_high_bandw_orien_measur_contr
- Increased robustness: cite:collette15_sensor_fusion_method_high_perfor
- Decrease the noise:
+In some situations, it is used to increase the bandwidth of the sensor cite:shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr,min15_compl_filter_desig_angle_estim.
+For instance by increasing the high frequency bandwidth of a position sensor using an accelerometer.

-*** Applications                                                    :ignore:
+
+Decrease the noise: cite:hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system,plummer06_optim_compl_filter_their_applic_motion_measur
+  [[cite:robert12_introd_random_signal_applied_kalman][chapter 8]]
+
+
+Increased robustness (sensor measuring different quantities): cite:collette15_sensor_fusion_method_high_perfor,yong16_high_speed_vertic_posit_stage
+
+\par
+
+** Applications                                                      :ignore:

 # The applications of sensor fusion are numerous
+The applications of sensor fusion are numerous.
+It is widely used for attitude estimation of unmanned aerial vehicle
+cite:baerveldt97_low_cost_low_weigh_attit,pascoal99_navig_system_desig_using_time,corke04_inert_visual_sensin_system_small_auton_helic,batista10_optim_posit_veloc_navig_filter_auton_vehic,jensen13_basic_uas,min15_compl_filter_desig_angle_estim

- UAV: cite:pascoal99_navig_system_desig_using_time, cite:jensen13_basic_uas
- Gravitational wave observer: cite:hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system,lucia18_low_frequen_optim_perfor_advan,heijningen18_low,akutsu21_vibrat_isolat_system_beam_split
+Motion control
+cite:shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr

-*** Kalman Filtering or Complementary filters                       :ignore:
+Tjepkema et al. cite:tjepkema12_sensor_fusion_activ_vibrat_isolat_precis_equip used sensor fusion to isolate precision equipment from the ground motion.

-# There are mainly two ways to perform sensor fusion: using complementary filters or using Kalman filtering
+Gravitational wave observer cite:heijningen18_low:
+LIGO cite:hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system
+VIRGO cite:lucia18_low_frequen_optim_perfor_advan

- cite:brown72_integ_navig_system_kalman_filter alternate form of complementary filters => Kalman filtering
- cite:higgins75_compar_compl_kalman_filter Compare Kalman Filtering with sensor fusion using complementary filters
- cite:robert12_introd_random_signal_applied_kalman advantage of complementary filters over Kalman filtering
+** Kalman Filtering / Complementary filters                          :ignore:

-*** Design Methods of Complementary filters                         :ignore:
+There are mainly two ways to perform sensor fusion: using complementary filters or using Kalman filtering cite:brown72_integ_navig_system_kalman_filter.
+Kalman filtering cite:odry18_kalman_filter_mobil_robot_attit_estim

-# In some cases, complementary filters are implemented in an analog way such as in [...], but most of the time it is implemented numerically which allows much more complex
+Relations between CF and Kalman: cite:becker15_compl_filter_desig_three_frequen_bands

- Analog complementary filters: cite:yong16_high_speed_vertic_posit_stage, cite:moore19_capac_instr_sensor_fusion_high_bandw_nanop
+Advantages of complementary filtering over Kalman filtering for sensor fusion:
+- Less computation cite:higgins75_compar_compl_kalman_filter
+- For Kalman filtering, we are forced to make assumption about the probabilistic character of the sensor noises cite:robert12_introd_random_signal_applied_kalman
+- More intuitive frequency domain technique

-# Multiple design methods have been used for complementary filters
+** Design Methods of Complementary filters                           :ignore:
+
+In some cases, complementary filters are implemented in an analog way such as in cite:yong16_high_speed_vertic_posit_stage,moore19_capac_instr_sensor_fusion_high_bandw_nanop, but most of the time it is implemented numerically which allows much more complex
+
+Multiple design methods have been used for complementary filters

 - Analytical methods:
-  - first order: cite:corke04_inert_visual_sensin_system_small_auton_helic
+  - first order: cite:corke04_inert_visual_sensin_system_small_auton_helic,yong16_high_speed_vertic_posit_stage
  - second order: cite:baerveldt97_low_cost_low_weigh_attit, cite:stoten01_fusion_kinet_data_using_compos_filter, cite:jensen13_basic_uas
  - higher order: cite:shaw90_bandw_enhan_posit_measur_using_measur_accel, cite:zimmermann92_high_bandw_orien_measur_contr, cite:collette15_sensor_fusion_method_high_perfor, cite:matichard15_seism_isolat_advan_ligo
 - cite:pascoal99_navig_system_desig_using_time use LMI to generate complementary filters (convex optimization techniques), specific for navigation systems
@@ -131,127 +152,26 @@ Sensor fusion \sep{} Optimal filters \sep{} $\mathcal{H}_\infty$ synthesis \sep{

 - 3 complementary filters: cite:becker15_compl_filter_desig_three_frequen_bands

-*** Problematics / gap in the research                              :ignore:
+** Problematic / gap in the research                                 :ignore:

- Robustness problems: cite:zimmermann92_high_bandw_orien_measur_contr change of phase near the merging frequency
+- Robustness problems: cite:zimmermann92_high_bandw_orien_measur_contr,plummer06_optim_compl_filter_their_applic_motion_measur change of phase near the merging frequency
 - Trial and error
 - Although many design methods of complementary filters have been proposed in the literature, no simple method that allows to shape the norm of the complementary filters is available.

-*** Describe the paper itself / the problem which is addressed      :ignore:
+** Describe the paper itself / the problem which is addressed        :ignore:

 Most of the requirements => shape of the complementary filters
 => propose a way to shape complementary filters.

-*** Introduce Each part of the paper                                :ignore:
+** Introduce Each part of the paper                                  :ignore:

-** Old Introduction                                         :ignore:noexport:
-*** Establish the importance of the research topic                 :ignore:
-# What are Complementary Filters
-A set of filters is said to be complementary if the sum of their transfer functions is equal to one at all frequencies.
-These filters are used when two or more sensors are measuring the same physical quantity with different noise characteristics.
-Unreliable frequencies of each sensor are filtered out by the complementary filters and then combined to form a super sensor giving a better estimate of the physical quantity over a wider bandwidth.
-This technique is called sensor fusion and is used in many applications.\par
+Section ref:sec:requirements

-*** Applications of complementary filtering                        :ignore:
-# Improve bandwidth for UAV
-In cite:zimmermann92_high_bandw_orien_measur_contr,corke04_inert_visual_sensin_system_small_auton_helic, various sensors (accelerometers, gyroscopes, vision sensors, etc.) are merged using complementary filters for the attitude estimation of Unmanned Aerial Vehicles (UAV).
-# Improving the control robustness
-In cite:collette15_sensor_fusion_method_high_perfor, several sensor fusion configurations using different types of sensors are discussed in order to increase the control bandwidth of active vibration isolation systems.
-# Merging of different sensor types
-Furthermore, sensor fusion is used in the isolation systems of the Laser Interferometer Gravitational-Wave Observator (LIGO) to merge inertial sensors with relative sensors
-cite:matichard15_seism_isolat_advan_ligo,hua04_polyp_fir_compl_filter_contr_system. \par
+Section ref:sec:hinf_method

-*** Current design methods for complementary filters               :ignore:
-# Why Design of Complementary Filter is important
-As the super sensor noise characteristics largely depend on the complementary filter norms, their proper design is of primary importance for sensor fusion.
-# Discuss the different approach to complementary filter design
-In cite:corke04_inert_visual_sensin_system_small_auton_helic,jensen13_basic_uas, first and second order analytical formulas of complementary filters have been presented.
-# Third Order and Higher orders
-Higher order complementary filters have been used in
-cite:shaw90_bandw_enhan_posit_measur_using_measur_accel,zimmermann92_high_bandw_orien_measur_contr,collette15_sensor_fusion_method_high_perfor.
-# Alternate Formulation
-In cite:jensen13_basic_uas, the sensitivity and complementary sensitivity transfer functions of a feedback architecture have been proposed to be used as complementary filters. The design of such filters can then benefit from the classical control theory developments.
-# LMI / convex Optimization
-Linear Matrix Inequalities (LMIs) are used in cite:pascoal99_navig_system_desig_using_time for the synthesis of complementary filters satisfying some frequency-like performance.
-# FIR Filters
-Finally, a synthesis method of high order Finite Impulse Response (FIR) complementary filters using convex optimization has been developed in cite:hua05_low_ligo,hua04_polyp_fir_compl_filter_contr_system. \par
+Section ref:sec:application_ligo

-*** Describe a gap in the research                                 :ignore:
-# There is a need for easy synthesis methods for complementary filters
-Although many design methods of complementary filters have been proposed in the literature, no simple method that allows to shape the norm of the complementary filters is available.
-
-*** Describe the paper itself / the problem which is addressed     :ignore:
-# In this paper, we propose a synthesis method for the shaping of complementary filters using the $\mathcal{H}_\infty$ norm.\par
-This paper presents a new design method of complementary filters based on $\mathcal{H}_\infty$ synthesis.
-This design method permits to easily shape the norms of the generated filters.\par
-
-*** Introduce Each part of the paper                               :ignore:
-The section ref:sec:requirements gives a brief overview of sensor fusion using complementary filters and explains how the typical requirements for such fusion can be expressed as upper bounds on the filters norms.
-In section ref:sec:hinf_method, a new design method for the shaping of complementary filters using $\mathcal{H}_\infty$ synthesis is proposed.
-In section ref:sec:application_ligo, the method is used to design complex complementary filters that are used for sensor fusion at the LIGO.
-Our conclusions are drawn in the final section.
-
-** Mohit's Introduction                                            :noexport:
-The sensors used for measuring physical quantity often works well within a limited frequency range called as the bandwidth of the sensor.
-The signals recorded by the sensor beyond its bandwidth are often corrupt with noise and are not reliable.
-Many dynamical systems require measurements over a wide frequency range.
-Very often a variety of sensors are utilized to sense the same quantity.
-These sensors have different operational bandwidth and are reliable only in a particular frequency range.
-The signals from the different sensors are fused together in order to get the reliable measurement of the physical quantity over wider frequency band.
-The combining of signals from various sensor is called sensor fusion cite:hua04_polyp_fir_compl_filter_contr_system.
-The resulting sensor is referred as "super sensor" since it can have better noise characteristics and can operate over a wider frequency band as compared to the individual sensor used for merging cite:shaw90_bandw_enhan_posit_measur_using_measur_accel.
-
-Sensor fusion is most commonly employed in the navigation systems to accurately measure the position of a vehicle.
-The GPS sensors, which are accurate in low frequency band, are merged with the high-frequency accelerometers.
-Zimmermann and Sulzer cite:zimmermann92_high_bandw_orien_measur_contr used sensor fusion to measure the orientation of a robot.
-They merged inclinometer and accelerometers for accurate angular measurements over large frequency band.
-Corke cite:corke04_inert_visual_sensin_system_small_auton_helic merged inertial measurement unit with the stereo vision system for measurement of attitude, height and velocity of an unmanned helicopter.
-Baerveldt and Klang cite:baerveldt97 used an inclinometer and a gyroscope to measure the orientation of the autonomous helicopter.
-The measurement of the 3D orientation using a gyroscope and an accelerometer was demonstrated by Roberts et al. cite:roberts03_low.
-Cao et al. cite:cao20_adapt_compl_filter_based_post used sensor fusion to obtain the lateral and longitudinal velocities of the autonomous vehicle.
-
-Sensor fusion is also used for enhancing the working range of the active isolation system.
-For example, the active vibration isolation system at the Laser Interferometer Gravitational-Wave Observatory (LIGO) cite:matichard15_seism_isolat_advan_ligo utilizes sensor fusion.
-The position sensors, seismometer and geophones are used for measuring the motion of the LIGO platform in different frequency bands cite:hua05_low_ligo.
-Tjepkema et al. cite:tjepkema12_sensor_fusion_activ_vibrat_isolat_precis_equip used sensor fusion to isolate precision equipment from the ground motion.
-The feedback from the accelerometer was used for active isolation at low frequency while force sensor was used at high frequency.
-Various configurations of sensor fusion for active vibration isolation systems are discussed by Collette and Matichard cite:collette15_sensor_fusion_method_high_perfor.
-Ma and Ghasemi-Nejhad cite:ma04_frequen_weigh_adapt_contr_simul used laser sensor and piezoelectric patches for simultaneous tracking and vibration control in smart structures.
-Recently, Verma et al. cite:verma21_virtual_sensor_fusion_high_precis_contr presented virtual sensor fusion for high precision control where the signals from a physical sensor are fused with a sensor simulated virtually.
-
-Fusing signals from different sensors can typically be done using Kalman filtering cite:odry18_kalman_filter_mobil_robot_attit_estim, ren19_integ_gnss_hub_motion_estim, faria19_sensor_fusion_rotat_motion_recon, liu18_innov_infor_fusion_method_with, abdel15_const_low_cost_gps_filter, biondi17_attit_recov_from_featur_track or complementary filters cite:brown72_integ_navig_system_kalman_filter.
-A set of filters is said to be complementary if the sum of their transfer functions is equal to one at all frequencies.
-When two filters are complementary, usually one is a low pass filter while the other is an high pass filter.
-The complementary filters are designed in such a way that their magnitude is close to one in the bandwidth of the sensor they are combined with.
-This enables to measure the physical quantity over larger bandwidth.
-There are two different categories of complementary filters --- frequency domain complementary filters and state space complementary filters.
-Earliest application of the frequency domain complementary filters was seen in Anderson and Fritze cite:anderson53_instr_approac_system_steer_comput.
-A simple RC circuit was used to physically realize the complementary filters.
-Frequency domain complementary filters were also used in cite:shaw90_bandw_enhan_posit_measur_using_measur_accel, zimmermann92_high_bandw_orien_measur_contr, baerveldt97, roberts03_low.
-State space complementary filter finds application in tracking orientation of the flexible links in a robot cite:bachmann03_desig_marg_dof,salcudean91_global_conver_angul_veloc_obser,mahony08_nonlin_compl_filter_special_orthog_group and are particularly useful for multi-input multi-output systems.
-Pascoal et al. cite:pascoal00_navig_system_desig_using_time presented complementary filters which can adapt with time for navigation system capable of estimating position and velocity using GPS and SONAR sensors.
-
-The noise characteristics of the super sensor are governed by the norms of the complementary filters.
-Therefore, the proper design of the complementary filters for sensor fusion is of immense importance.
-The design of complementary filters is a complex task as they need to tuned as per the specification of the sensor.
-In many applications, analytical formulas of first and second order complementary filters are used cite:corke04_inert_visual_sensin_system_small_auton_helic,jensen13_basic_uas.
-However, these low order complementary filters are not optimal, and high order complementary filters can lead to better fusion cite:jensen13_basic_uas,shaw90_bandw_enhan_posit_measur_using_measur_accel.
-
-Several design techniques have been proposed to design higher order complementary filters.
-Pascoal cite:pascoal00_navig_system_desig_using_time used linear matrix inequalities (LMIs) cite:boyd94_linear for the design of time varying complementary filters.
-LMIs were also used by Hua et al. cite:hua04_polyp_fir_compl_filter_contr_system to design finite impulse response (FIR) filters for the active vibration isolation system at LIGO.
-Plummer cite:plummer06_optim_compl_filter_their_applic_motion_measur proposed an optimal design method using the $\mathcal{H}_{\infty}$ synthesis and weighting functions representing the measurement noise of the sensors.
-
-Although various methods have been presented in the literature for the design of complementary filters, there is a lack of general and simple framework that allows to shape the norm of complementary filters.
-Such a method would prove to be very useful as the noise of the "supper sensor" and its dynamical characteristics depend on the norm of the filters.
-This paper presents such a framework based on the $\mathcal{H}_\infty$ norm minimization.
-The proposed method is quite general and can be easily extended to a case where more than two complementary filters needs to be designed.
-The organization of this paper is as follows.
-Section 2 presents the design requirements of ideal complementary filters.
-It also demonstrates how the noise and robustness characteristics of the "super sensor" can be transformed into upper bounds on the norm of the complementary filters.
-The framework for the design of complementary filters is detailed in Section 3.
-This is followed by the application of the design method to complementary filter design for the active vibration isolation at LIGO in Section 4.
-Finally, concluding remarks are presented in Section 5.
+Section ref:sec:discussion

 * Sensor Fusion and Complementary Filters Requirements
 <<sec:requirements>>
@@ -367,7 +287,7 @@ As shown in eqref:eq:noise_filtering_psd, the Power Spectral Density (PSD) of th
 \end{equation}

 If the two sensors have identical noise characteristics ($\Phi_{n_1}(\omega) = \Phi_{n_2}(\omega)$), a simple averaging ($H_1(s) = H_2(s) = 0.5$) is what would minimize the super sensor noise.
-This the simplest form of sensor fusion with complementary filters.
+This is the simplest form of sensor fusion with complementary filters.

 However, the two sensors have usually high noise levels over distinct frequency regions.
 In such case, to lower the noise of the super sensor, the value of the norm $|H_1|$ has to be lowered when $\Phi_{n_1}$ is larger than $\Phi_{n_2}$ and that of $|H_2|$ lowered when $\Phi_{n_2}$ is larger than $\Phi_{n_1}$.
@@ -557,7 +477,7 @@ The magnitudes of the weighting functions are shown by dashed lines in Figure re
 #+name: tab:weights_params
 #+caption: Parameters used for weighting functions $W_1(s)$ and $W_2(s)$ using eqref:eq:weight_formula
 #+ATTR_LATEX: :environment tabularx :width 0.29\linewidth :align ccc
-#+ATTR_LATEX: :center t :booktabs t :float t
+#+ATTR_LATEX: :center t :booktabs t
 | Parameters | $W_1(s)$      | $W_2(s)$      |
 |------------+---------------+---------------|
 | $G_0$      | $0.1$         | $1000$        |
@@ -597,7 +517,7 @@ A more complex real life example is taken up in the next section.
 <<sec:application_ligo>>
 ** Introduction                                                     :ignore:

-Sensor fusion using complementary filters are widely used in active vibration isolation systems in gravitational wave detectors such at the LIGO cite:matichard15_seism_isolat_advan_ligo,hua05_low_ligo, the VIRGO cite:lucia18_low_frequen_optim_perfor_advan,heijningen18_low and the KAGRA cite:akutsu21_vibrat_isolat_system_beam_split.
+Sensor fusion using complementary filters are widely used in active vibration isolation systems in gravitational wave detectors such at the LIGO cite:matichard15_seism_isolat_advan_ligo,hua05_low_ligo, the VIRGO cite:lucia18_low_frequen_optim_perfor_advan,heijningen18_low and the KAGRA [[cite:sekiguchi16_study_low_frequen_vibrat_isolat_system][Chap. 5]].

 In the first isolation stage at the LIGO, two sets of complementary filters are used and included in a feedback loop cite:hua04_low_ligo.
 A set of complementary filters ($L_2,H_2$) is first used to fuse a seismometer and a geophone.
@@ -630,6 +550,8 @@ The specifications for the set of complementary filters ($L_1,H_1$) used at the
 These specifications are therefore upper bounds on the complementary filters' magnitudes.
 They are physically represented in Figure ref:fig:fir_filter_ligo as well as the obtained magnitude of the FIR filters in cite:hua05_low_ligo.

+# Replicated using SeDuMi matlab toolbox cite:sturm99_using_sedum
+
 #+name: fig:fir_filter_ligo
 #+caption: Specifications and Bode plot of the obtained FIR filters in cite:hua05_low_ligo
 #+attr_latex: :scale 1
@@ -766,6 +688,8 @@ Link to literature about doing that with mixed sensitivity
 Some applications may require to merge more than two sensors.
 For instance at the LIGO, three sensors (an LVDT, a seismometer and a geophone) are merged to form a super sensor (Figure ref:fig:ligo_super_sensor_architecture). \par

+- [ ] cite:becker15_compl_filter_desig_three_frequen_bands
+
 *** Sequential vs Parallel                                          :ignore:
 When merging $n>2$ sensors using complementary filters, two architectures can be used as shown in Figure ref:fig:sensor_fusion_three.

--- a/journal/ref.bib
+++ b/journal/ref.bib
@@ -7,7 +7,6 @@
  pages           = {1-21},
  year            = 2015,
  doi             = {10.1016/j.jsv.2015.01.006},
-  url             = {https://doi.org/10.1016/j.jsv.2015.01.006},
 }

@phdthesis{hua05_low_ligo,
@@ -15,15 +14,14 @@
  doi             = {10.1117/12.552518},
  school          = {stanford university},
  title           = {Low frequency vibration isolation and alignment system for
-                  advanced LIGO},
-  url             = {https://doi.org/10.1117/12.552518},
+                  advanced {LIGO}},
  year            = 2005,
 }

@inproceedings{hua04_polyp_fir_compl_filter_contr_system,
  author          = {Hua, Wensheng and Debra, B. and Hardham, T. and Lantz, T.
                  and Giaime, A.},
-  title           = {Polyphase FIR Complementary Filters for Control Systems},
+  title           = {Polyphase {FIR} Complementary Filters for Control Systems},
  booktitle       = {Proceedings of ASPE Spring Topical Meeting on Control of
                  Precision Systems},
  year            = 2004,
@@ -33,7 +31,7 @@
@article{matichard15_seism_isolat_advan_ligo,
  author          = {Matichard, F and Lantz, B and Mittleman, R and Mason, K and
                  Kissel, J and others},
-  title           = {Seismic Isolation of Advanced Ligo: Review of Strategy,
+  title           = {Seismic Isolation of Advanced {LIGO}: Review of Strategy,
                  Instrumentation and Performance},
  journal         = {Classical and Quantum Gravity},
  volume          = 32,
@@ -41,7 +39,6 @@
  pages           = 185003,
  year            = 2015,
  doi             = {10.1088/0264-9381/32/18/185003},
-  url             = {https://doi.org/10.1088/0264-9381/32/18/185003},
  publisher       = {IOP Publishing},
 }

@@ -55,7 +52,6 @@
  pages           = {43-51},
  year            = 2004,
  doi             = {10.1002/rob.10127},
-  url             = {https://doi.org/10.1002/rob.10127},
 }

@inproceedings{jensen13_basic_uas,
@@ -66,7 +62,6 @@
                  (ICUAS)},
  year            = 2013,
  doi             = {10.1109/ICUAS.2013.6564726 },
-  url             = {https://doi.org/10.1109/ICUAS.2013.6564726 },
  month           = 5,
 }

@@ -77,7 +72,6 @@
  booktitle       = {Guidance, Navigation, and Control Conference and Exhibit},
  year            = 1999,
  doi             = {10.1109/7.892661},
-  url             = {https://doi.org/10.1109/7.892661},
 }

@article{zimmermann92_high_bandw_orien_measur_contr,
@@ -88,27 +82,13 @@
  pages           = {525-530},
  year            = 1992,
  doi             = {10.1016/B978-0-08-041276-4.50093-5},
-  url             = {https://doi.org/10.1016/B978-0-08-041276-4.50093-5},
  publisher       = {Elsevier},
  series          = {Robot Control 1991},
 }

-@article{mahony08_nonlin_compl_filter_special_orthog_group,
-  author          = {Robert Mahony and Tarek Hamel and Jean-Michel Pflimlin},
-  title           = {Nonlinear Complementary Filters on the Special Orthogonal
-                  Group},
-  journal         = {IEEE Transactions on Automatic Control},
-  volume          = 53,
-  number          = 5,
-  pages           = {1203-1218},
-  year            = 2008,
-  doi             = {10.1109/TAC.2008.923738},
-  url             = {https://doi.org/10.1109/TAC.2008.923738},
-}
-
@article{sturm99_using_sedum,
  author          = {Sturm, Jos F},
-  title           = {Using Sedumi 1.02, a Matlab Toolbox for Optimization Over
+  title           = {Using {SeDuMi} 1.02, a Matlab Toolbox for Optimization Over
                  Symmetric Cones},
  journal         = {Optimization methods and software},
  volume          = 11,
@@ -116,25 +96,9 @@
  pages           = {625--653},
  year            = 1999,
  doi             = {10.1080/10556789908805766},
-  url             = {https://doi.org/10.1080/10556789908805766},
  publisher       = {Taylor \& Francis},
 }

-@article{cao20_adapt_compl_filter_based_post,
-  author          = {Cao, Mingcong and Hu, Chuan and Wang, Jinxiang and Wang,
-                  Rongrong and Chen, Nan},
-  title           = {Adaptive Complementary Filter-Based Post-Impact Control for
-                  Independently-Actuated and Differentially-Steered Autonomous
-                  Vehicles},
-  journal         = {Mechanical Systems and Signal Processing},
-  volume          = 144,
-  pages           = 106852,
-  year            = 2020,
-  doi             = {10.1016/j.ymssp.2020.106852},
-  url             = {https://doi.org/10.1016/j.ymssp.2020.106852},
-  publisher       = {Elsevier},
-}
-
@article{tjepkema12_sensor_fusion_activ_vibrat_isolat_precis_equip,
  author          = {Tjepkema, D and van Dijk, Johannes and Soemers, HMJR},
  title           = {Sensor Fusion for Active Vibration Isolation in Precision
@@ -145,38 +109,9 @@
  pages           = {735--749},
  year            = 2012,
  doi             = {10.1016/j.jsv.2011.09.022},
-  url             = {https://doi.org/10.1016/j.jsv.2011.09.022},
  publisher       = {Elsevier},
 }

-@article{verma21_virtual_sensor_fusion_high_precis_contr,
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-@article{ma04_frequen_weigh_adapt_contr_simul,
-  author          = {Ma, Kougen and Ghasemi-Nejhad, Mehrdad N},
-  title           = {Frequency-Weighted Adaptive Control for Simultaneous
-                  Precision Positioning and Vibration Suppression of Smart
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-}
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@@ -186,7 +121,6 @@
  pages           = {219--228},
  year            = 1953,
  doi             = {10.1109/JRPROC.1953.274209},
-  url             = {https://doi.org/10.1109/JRPROC.1953.274209},
  publisher       = {IEEE},
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  pages           = {23--38},
  year            = 1990,
  doi             = {10.1016/0888-3270(90)90038-M},
-  url             = {https://doi.org/10.1016/0888-3270(90)90038-M},
  publisher       = {Elsevier},
 }

-@inproceedings{baerveldt97,
-  author          = {Baerveldt, A-J and Klang, Robert},
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-                  Engineering Systems},
-  year            = 1997,
-  pages           = {391--395},
-  doi             = {10.1109/INES.1997.632450},
-  url             = {https://doi.org/10.1109/INES.1997.632450},
-  organization    = {IEEE},
-}
-
-@inproceedings{roberts03_low,
-  author          = {Roberts, Jonathan M and Corke, Peter I and Buskey, Gregg},
-  title           = {Low-cost flight control system for a small autonomous
-                  helicopter},
-  booktitle       = {2003 IEEE International Conference on Robotics and
-                  Automation (Cat. No. 03CH37422)},
-  year            = 2003,
-  volume          = 1,
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-  doi             = {10.1109/ROBOT.2003.1241651},
-  url             = {https://doi.org/10.1109/ROBOT.2003.1241651},
-  organization    = {IEEE},
-}
-
-@inproceedings{bachmann03_desig_marg_dof,
-  author          = {Bachmann, Eric R and Yun, Xiaoping and McKinney, Doug and
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-  title           = {Design and implementation of MARG sensors for 3-DOF
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-  booktitle       = {2003 IEEE International Conference on Robotics and
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-  organization    = {IEEE},
-}
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-@article{salcudean91_global_conver_angul_veloc_obser,
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-  author          = {Pascoal, Antonio and Kaminer, Isaac and Oliveira, Paulo},
-  title           = {Navigation System Design Using Time-Varying Complementary
-                  Filters},
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-  organization    = {IEEE},
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  year            = 2006,
  doi             = {10.1243/09596518JSCE229},
-  url             = {https://doi.org/10.1243/09596518JSCE229},
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 }

@@ -311,70 +161,9 @@
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  year            = 2018,
  doi             = {10.1016/j.ymssp.2018.03.053},
-  url             = {https://doi.org/10.1016/j.ymssp.2018.03.053},
  publisher       = {Elsevier},
 }

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-                  Zhen and Verma, Amrit Shankar},
-  title           = {Integrated Gnss/imu Hub Motion Estimator for Offshore Wind
-                  Turbine Blade Installation},
-  journal         = {Mechanical Systems and Signal Processing},
-  volume          = 123,
-  pages           = {222--243},
-  year            = 2019,
-  doi             = {10.1016/j.ymssp.2019.01.008},
-  url             = {https://doi.org/10.1016/j.ymssp.2019.01.008},
-  publisher       = {Elsevier},
-}
-
-@article{faria19_sensor_fusion_rotat_motion_recon,
-  author          = {Faria, CT},
-  title           = {Sensor Fusion and Rotational Motion Reconstruction Via
-                  Nonlinear State-Observers},
-  journal         = {Mechanical Systems and Signal Processing},
-  volume          = 114,
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-  publisher       = {Elsevier},
-}
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-                  Li, Liang and Ding, Dawei},
-  title           = {An Innovative Information Fusion Method With Adaptive
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-}
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-}
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  author          = {MATLAB},
  title           = {version 9.9.0 (R2020b)},
@@ -383,34 +172,6 @@
  address         = {Natick, Massachusetts},
 }

-@article{abdel15_const_low_cost_gps_filter,
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-                  Mohammad Amin},
-  title           = {Constrained Low-Cost Gps/ins Filter With Encoder Bias
-                  Estimation for Ground Vehicles Applications},
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-  volume          = 58,
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-  doi             = {10.1016/j.ymssp.2014.12.012},
-  url             = {https://doi.org/10.1016/j.ymssp.2014.12.012},
-  publisher       = {Elsevier},
-}
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-@article{biondi17_attit_recov_from_featur_track,
-  author          = {Biondi, Gabriele and Mauro, S and Mohtar, T and Pastorelli,
-                  S and Sorli, M},
-  title           = {Attitude Recovery From Feature Tracking for Estimating
-                  Angular Rate of Non-Cooperative Spacecraft},
-  journal         = {Mechanical Systems and Signal Processing},
-  volume          = 83,
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-  doi             = {10.1016/j.ymssp.2016.06.017},
-  url             = {https://doi.org/10.1016/j.ymssp.2016.06.017},
-  publisher       = {Elsevier},
-}
-
@article{brown72_integ_navig_system_kalman_filter,
  author          = {R. G. Brown},
  title           = {Integrated Navigation Systems and Kalman Filtering: a
@@ -421,7 +182,6 @@
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  year            = 1972,
  doi             = {10.1002/j.2161-4296.1972.tb01706.x},
-  url             = {https://doi.org/10.1002/j.2161-4296.1972.tb01706.x},
 }

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@@ -434,7 +194,6 @@
  pages           = {1-3},
  year            = 2019,
  doi             = {10.1109/lsens.2019.2933065},
-  url             = {https://doi.org/10.1109/lsens.2019.2933065},
 }

@article{higgins75_compar_compl_kalman_filter,
@@ -445,8 +204,6 @@
  pages           = {321--325},
  year            = 1975,
  doi             = {10.1109/TAES.1975.308081},
-  url             = {https://doi.org/10.1109/TAES.1975.308081},
-  keywords        = {sensor fusion, complementary filters},
  publisher       = {IEEE},
 }

@@ -456,8 +213,12 @@
                  with Matlab Exercises},
  year            = 2012,
  publisher       = {Wiley},
+  day             = 07,
  edition         = 4,
  isbn            = {978-0-470-60969-9},
+  month           = 02,
+  note            = {isbn:978-0-470-60969-9},
+  pagecount       = 400,
 }

@inproceedings{becker15_compl_filter_desig_three_frequen_bands,
@@ -469,9 +230,8 @@
                  Competitions},
  year            = 2015,
  doi             = {10.1109/ICARSC.2015.34},
-  url             = {https://doi.org/10.1109/ICARSC.2015.34},
  isbn            = {978-1-4673-6991-6},
-  keywords        = {complementary filters},
+  note            = {isbn:978-1-4673-6991-6},
  page            = {204--209},
 }

@@ -484,7 +244,6 @@
  pages           = "184 - 192",
  year            = 2016,
  doi             = {10.1016/j.sna.2016.06.042},
-  url             = {https://doi.org/10.1016/j.sna.2016.06.042},
  issn            = "0924-4247",
 }

@@ -497,8 +256,6 @@
  year            = 1997,
  pages           = {nil},
  doi             = {10.1109/ines.1997.632450},
-  url             = {https://doi.org/10.1109/ines.1997.632450},
-  keywords        = {complementary filters},
  month           = {-},
 }

@@ -512,8 +269,6 @@
  pages           = {483--497},
  year            = 2001,
  doi             = {10.1177/095965180121500505},
-  url             = {https://doi.org/10.1177/095965180121500505},
-  keywords        = {sensor fusion, complementary filters},
  publisher       = {SAGE Publications Sage UK: London, England},
 }

@@ -524,7 +279,6 @@
  journal         = {IRE Transactions on Circuit Theory},
  year            = 1957,
  doi             = {10.1109/tct.1957.1086345},
-  url             = {https://doi.org/10.1109/tct.1957.1086345},
  page            = {--},
 }

@@ -533,7 +287,7 @@
                  Giaime, Joseph A and Hammond, Giles Dominic and Hardham, C and
                  Hennessy, Mike and How, Jonathan P and Lantz, Brian T and
                  Macinnis, M and others},
-  title           = {Low-frequency active vibration isolation for advanced LIGO},
+  title           = {Low-frequency active vibration isolation for advanced {LIGO}},
  booktitle       = {Gravitational Wave and Particle Astrophysics Detectors},
  year            = 2004,
  volume          = 5500,
@@ -541,21 +295,6 @@
  organization    = {International Society for Optics and Photonics},
 }

-@article{akutsu21_vibrat_isolat_system_beam_split,
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-                  and Araya, A and Aritomi, N and Asada, H and Aso, Y and Bae, S
-                  and others},
-  title           = {Vibration Isolation Systems for the Beam Splitter and
-                  Signal Recycling Mirrors of the Kagra Gravitational Wave
-                  Detector},
-  journal         = {Classical and Quantum Gravity},
-  volume          = 38,
-  number          = 6,
-  pages           = 065011,
-  year            = 2021,
-  publisher       = {IOP Publishing},
-}
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  author          = {van Heijningen, JV},
  school          = {Vrije Universiteit},
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  year            = 2005,
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  doi             = {10.1109/cdc.2005.1582367},
-  url             = {https://doi.org/10.1109/cdc.2005.1582367},
  month           = {-},
 }
+
+@article{min15_compl_filter_desig_angle_estim,
+  author          = {Min, Hyung Gi and Jeung, Eun Tae},
+  title           = {Complementary Filter Design for Angle Estimation Using Mems
+                  Accelerometer and Gyroscope},
+  journal         = {Department of Control and Instrumentation, Changwon
+                  National University, Changwon, Korea},
+  pages           = {641--773},
+  year            = 2015,
+}
+
+@article{batista10_optim_posit_veloc_navig_filter_auton_vehic,
+  author          = {Batista, Pedro and Silvestre, Carlos and Oliveira, Paulo},
+  title           = {Optimal Position and Velocity Navigation Filters for
+                  Autonomous Vehicles},
+  journal         = {Automatica},
+  volume          = 46,
+  number          = 4,
+  pages           = {767--774},
+  year            = 2010,
+  doi             = {10.1016/j.automatica.2010.02.004},
+  publisher       = {Elsevier},
+}
+
+@phdthesis{sekiguchi16_study_low_frequen_vibrat_isolat_system,
+  author          = {Sekiguchi, Takanori},
+  school          = {University of Tokyo},
+  title           = {A Study of Low Frequency Vibration Isolation System for
+                  large scale gravitational wave detectors},
+  year            = 2016,
+}