# Comparison of Complementary Filters Implementations for Unmanned Aerial Vehicles

02 Dec 2022-pp 1-4

TL;DR: In this paper , different configurations of analog filters are tested in order to possibly find a better solution than that offered by the algorithm, used in many low-cost applications of Unmanned Aerial Vehicle positional awareness.

Abstract: This work deals with the comparison between different analog implementations of complementary filters with a digital processing algorithm. For this purpose, different configurations of analog filters are tested in order to possibly find a better solution than that offered by the algorithm, used in many low-cost applications of Unmanned Aerial Vehicle positional awareness.

##### References

More filters

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14 Oct 2008TL;DR: A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation that is evaluated against the output from a full GPS/INS that was available for the data set.

Abstract: This paper considers the question of using a nonlinear complementary filter for attitude estimation of fixed-wing unmanned aerial vehicle (UAV) given only measurements from a low-cost inertial measurement unit. A nonlinear complementary filter is proposed that combines accelerometer output for low frequency attitude estimation with integrated gyrometer output for high frequency estimation. The raw accelerometer output includes a component corresponding to airframe acceleration, occurring primarily when the aircraft turns, as well as the gravitational acceleration that is required for the filter. The airframe acceleration is estimated using a simple centripetal force model (based on additional airspeed measurements), augmented by a first order dynamic model for angle-of-attack, and used to obtain estimates of the gravitational direction independent of the airplane manoeuvres. Experimental results are provided on a real-world data set and the performance of the filter is evaluated against the output from a full GPS/INS that was available for the data set.

488 citations

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16 Dec 1998TL;DR: In this paper, the authors present an approach for the estimation of 1st and 2nd-order functions of LC ladder filters using Opamps, which is based on the Bessel-Thomson Delay Approximation Delay Equalization Frequency Transformations.

Abstract: Fundamentals Introduction Filter Characterization Types of Filters Steps in Filter Design Analysis Continuous-Time Filter Functions Stability Passivity for One- and Two-Port Networks Reciprocity The Approximation Problem Introduction Filter Specifications and Permitted Functions Formulation of the Approximation Problem Approximation to the Ideal Lowpass Filter Filters with Linear Phase: Delays Bessel-Thomson Delay Approximation Delay Equalization Frequency Transformations Design Tables of Passive LC Ladder Filters Impedance Scaling Predistortion Active Elements Introduction Ideal Controlled Sources Impedance Transformation (Generalized Impedance Converters and Inverters) Negative Resistance Ideal Operational Amplifier The Ideal Operational Transconductance Amplifier (OTA) Realization of 1st- and 2nd-Order Functions Using Opamps Introduction Realization of 1st-Order Functions The General 2nd-Order Filter Function Sensitivity of 2nd-Order Filters Realization of Biquadratic Functions Using SABs Realization of a Quadratic with a Positive Real Zero Biquads Obtained Using the Twin-Tee RC Network Two Opamp Biquads Three Opamp Biquads Realization of High-Order Functions Using Opamps Introduction Selection Criteria for High-Order Function Realizations Mutliparameter Sensitivity High-Order Function Realization Methods Cascade Connection of 2nd-Order Sections Mutli-Loop Feedback Filters Cascade of Biquartics Simulation of LC Ladder Filters Using Opamps Introduction Resistively Terminated Lossless LC ladder Filters Methods of LC Ladder Filter Simulation The Gyrator Generalized Impedance Converter FDNRs Complex Impedance Scaling Functional Simulation Wave Active Filters Introduction Wave Active Filters Wave Active Equivalents (WAE) Economical Wave Active Filters Sensitivity of WAFs Operation of WAFs at Higher Frequencies Complementary Transfer Functions Wave Simulation of Inductance Linear Transformation Active Filters (LTA Filters)

377 citations

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TL;DR: In this paper, the authors present a tutorial for complementary filtering and show its relationship to Kalman and Wiener filtering. But they make no reference to Wiener or Kalman filters, although it is related to them.

Abstract: A technique used in the flight control industry for estimation when combining measurements is the complementary filter. This filter is usually designed without any reference to Wiener or Kalman filters, although it is related to them. This paper, which is mainly tutorial, reviews complementary filtering and shows its relationship to Kalman and Wiener filtering.

315 citations

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TL;DR: The mathematical background that is required for complementary time-varying filter analysis and design is introduced and its application to the design of a navigation system that estimates position and velocity of an autonomous vehicle by complementing position information available from GPS with the velocity information provided by a Doppler sonar system is described.

Abstract: A new methodology for the design of navigation systems for autonomous vehicles is introduced. Using simple kinematic relationships, the problem of estimating the velocity and position of an autonomous vehicle is solved by resorting to special bilinear time-varying filters. These are the natural generalization of linear time-invariant complementary filters that are commonly used to properly merge sensor information available at low frequency with that available in the complementary region. Complementary filters lend themselves to frequency domain interpretations that provide valuable insight into the filtering design process. This work extends these properties to the time-varying setting by resorting to the theory of linear differential inclusions and by converting the problem of weighted filter performance analysis into that of determining the feasibility of a related set of linear matrix inequalities (LMIs). Using this set-up, the stability of the resulting filters as well as their "frequency-like" performance can be assessed using efficient numerical analysis tools that borrow from convex optimization techniques. The mathematical background that is required for complementary time-varying filter analysis and design is introduced. Its application to the design of a navigation system that estimates position and velocity of an autonomous vehicle by complementing position information available from GPS with the velocity information provided by a Doppler sonar system is described.

74 citations

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TL;DR: This work addresses the complete state estimation problem of unmanned aerial vehicles, even under high-dynamic 3-D aerobatic maneuvers, while using low-cost sensors with bias variations and higher levels of noise, with the efficiency of a complementary filter scheme.

Abstract: We address the complete state estimation problem of unmanned aerial vehicles, even under high-dynamic 3-D aerobatic maneuvers, while using low-cost sensors with bias variations and higher levels of noise. In such conditions, the control demand, for a robust real-time data fusion filter with minimal lag and noise, is addressed with the efficiency of a complementary filter scheme. First, the attitude is directly estimated in Special Orthogonal Group (SO(3)) by complementing the noisy accelerometer/magnetometer vector basis with a gyro propagated vector basis. Data fusion follows a least square minimization in SO(3) (Wahba’s problem) solved in an analytic nonrecursive manner. Stability of the proposed filter is shown and performance metrics are extracted, whereas the computational complexity has been minimized with an appropriate reference frame and a custom singular value decomposition algorithm. An adaptation scheme is proposed to allow unhindered operation of the filter to erroneous inputs introduced by the high dynamics of a 3-D flight. Finally, the velocity/position estimation is mainly constructed by complementary filters combining multiple sensors. In addition to the low complexity and the filtering of the noise, the proposed observer is aided through a developed vision algorithm, enabling the use of the filter in Global-Positioning-System-denied environments. Extensive experimental results and comparative studies with state-of-the-art filters, either in the laboratory or in the field using high-performance autonomous helicopters, demonstrate the efficacy of the proposed scheme in demanding conditions.

74 citations