基于扩展卡尔曼滤波器的实用AHRS算法实现

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"Attitude and Heading Reference System的实现方法" Attitude and Heading Reference System（AHRS）是一种用于实时车辆导航、制导和控制应用的关键系统。该系统的实现对低成本传感器的使用提出了高效和鲁棒的算法要求。本文描述了一种实用且可靠的算法来实现AHRS，该算法基于扩展卡尔曼滤波器（EKF）在直接配置中。 AHRS是车辆导航、制导和控制系统的核心组件，它提供了车辆的姿态和方位信息。AHRS的实现需要考虑多个因素，包括传感器的选择、数据融合算法和计算机系统的设计。在低成本传感器的情况下，AHRS算法需要具备高效和鲁棒性，以确保系统的可靠性和准确性。本文提出的算法基于EKF在直接配置中，该算法可以实时计算车辆的姿态和方位信息。EKF是一种常用的状态估计算法，它可以结合多个传感器的数据，实时估计算车辆的状态。在AHRS系统中，EKF可以结合陀螺仪、加速度计和磁力计等传感器的数据，实时计算车辆的姿态和方位信息。 AHRS系统的实现需要考虑多个方面，包括： 1. 传感器的选择：AHRS系统需要选择合适的传感器，以确保系统的可靠性和准确性。常用的传感器包括陀螺仪、加速度计、磁力计等。 2. 数据融合算法：AHRS系统需要一种数据融合算法，以将多个传感器的数据融合成一个可靠的结果。EKF是一种常用的数据融合算法。 3. 计算机系统的设计：AHRS系统需要一个高效的计算机系统，以实时计算车辆的姿态和方位信息。 4. 算法的鲁棒性：AHRS系统需要一种鲁棒的算法，以确保系统的可靠性和准确性。本文提出的算法可以实现在低成本传感器的情况下实现AHRS，该算法基于EKF在直接配置中，具有高效和鲁棒性。该算法可以应用于车辆导航、制导和控制等领域。此外，AHRS系统还可以应用于其他领域，例如机器人、无人机、船舶等领域，该系统可以提供实时的姿态和方位信息，用于实现自动驾驶、自动避障等功能。本文提出的算法可以满足AHRS系统的需求，具有高效和鲁棒性，能够实现在低成本传感器的情况下实现AHRS。

predicted and current measurement). In this kind of EKF

configuration, the system is essentially derived from the

system kinematics. One of the characteristics of the direct

configuration is its conceptual clarity and simplicity. The

differences between both kinds of configurations can

considerably impact the development process of

applications based on the EKF. A good review of EKF and

its configurations can be found in [14].

In addition, it is possible to find other methods which

rely on variations of Kalman filtering, such as the

Unscented Kalman filtering [15]. Another interesting

family of methods for attitude estimation is nonlinear

observers [6,8]. Nonlinear observers often exhibit global

convergence which means they can converge from any

initial guess. A good review of several filtering methods

for attitude estimation is given in [16]. Another kind of

method relies on estimation techniques coming from the

artificial intelligence (AI) research community. For

instance in [9], attitude estimation relies on a digital

neural network.

Although it is possible to find different methodologies in

the literature, EKF is still the standard estimation

technique for attitude estimation. Nevertheless, the use of

EKF in direct configuration has been much less explored

than its counterpart, the EKF in indirect configuration

(see Table I). This happens especially when system errors

(e.g., gyro bias) are to be included in the vector state. An

example of direct linear Kalman filtering for attitude and

position estimation (GPS + Inertial navigation system) can

be found in [17]. Nevertheless, since this method is also

intended for position estimation, it is highly dependent

on GPS measurements, and thus limits its usability for

applications which rely solely on attitude estimation.

Moreover, the LTI (linear time invariant) approach of this

method can affect the performance of the estimations due

to the non-linear nature of the problem.

On the other hand, the EKF in its direct configuration has

been widely used (for about two decades) by the research

community on autonomous robots, to implement

methods of localization, mapping or both: SLAM

(Simultaneous Localization and Mapping), see [18].

The method presented in this work is motivated by the

necessity of having a practical and reliable method for

attitude and heading estimation that can be tightly

integrated with filter-based SLAM methods in a

straightforward manner. In this sense, it is important to

note that most of the currently available algorithms for

SLAM use a loosely-coupled approach for incorporating

attitude measurements. In other words, in a loosely-

coupled approach, the SLAM algorithm takes the attitude

and orientation estimated by an AHRS unit as a high-

level input. On the other hand, since the proposed

method was derived using the indirect configuration of

the EKF, it can be easily plugged into a filter-based SLAM

algorithm using a tightly-coupled approach. Thus, low-level

measurements (i.e., from gyros, accelerometers,

magnetometers) can be incorporated directly into the SLAM

scheme to aid in attitude and heading determination.

This paper considerably extends the authors' previous

work [19] where the idea of an AHRS based on an EKF in

a direct configuration is introduced. Some of the most

important additions included in this work are:

• A new (discrete) kinematic nonlinear model is used in

the prediction equations of the filter, in order to

improve the performance of the method when

operating at a low sample rate.

•The actual rotational speed of the body is included in

the system state vector, in order to improve the

observability of the bias of gyros.

• In order to detect instants when the body is in a

non-accelerating mode, the Stance Hypothesis

Optimal Detector (SHOE) [20] is used.

• A novel method is developed for updating yaw

(heading) measurements, in order to improve the

modularity and scalability of the method.

• In order to validate the performance of the proposed

method, a comparative study with real data is

presented, where the proposed method is compared

(in different conditions) with a related method. In

experiments, the high-performance miniature unit

3DM-GX3®45 from MicroStrain® is used as ground

truth.

The paper is organized as follows: in Section 2, the

proposed method is described. It is important to note that

the paper is presented in a self-contained style, since all

the required equations for implementing the proposed

method are included. Section 3 presents the experimental

results. In Section 4, the final remarks are presented. An

appendix with the transformations required for

implementing the proposed method is also included.

2. Method description

2.1 Vector state and system specification

The goal of the proposed method is to estimate the

following system state x



x x

nb b

′

 

 

(1)

where q

= [q

] is a unit quaternion representing

the orientation (roll, pitch and yaw) of the body (device);

= [ω

] is the bias-compensated velocity rotation of

the body expressed in the body frame; x

= [x

g_x

g_y

g_z

] is

the bias of gyros.

Rodrigo Munguía and Antoni Grau: A Practical Method for

Implementing an Attitude and Heading Reference System

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