Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms.

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Cascaded Complementary Filter Architecture for Sensor Fusion in Attitude Estimation

A generalized algorithm for spacecraft three-axis attitude and rate estimation is presented. The filter concept presented here is quite general and applicable to a wide range of sensor systems. The structure of the filter is built using spacecraft nonlinear dynamics in the presence of momentum exchange devices. Then quaternion kinematics are augmented with spacecraft dynamics to represent the filter process dynamics. Magnetometer measurements and their corresponding time derivatives are utilized to represent the filter measurement model. The filter is designed to suit spacecraft in low-Earth orbits, so the aerodynamic drag in addition to spacecraft magnetic residuals are included as disturbance torques to the process dynamics. To test the filter EgyptSat-1 is used as a real test case where a magnetometer and fiber-optic gyros are used for attitude and rate determination. A Monte-Carlo simulation is then applied to study the performance of the proposed filter with initial attitude and angular velocity sampled from a uniform distribution, each axis respectively. The filter shows the capability of estimating the attitude within 5 deg and rate on the order of 0.03 deg/s in each axis.

Sigma-Point Kalman Filtering for Spacecraft Attitude and Rate Estimation Using Magnetometer Measurements

Vehicular positioning systems are necessary for the development of autonomous vehicles and advanced driver assistance systems (ADAS). In recent years, Inertial Measurement Units (IMU) based on micro-electromechanical systems (MEMS) have been included in proposals for multi-sensor positioning system architectures in order to take advantage of their cost and size. The measurement errors propagation to the positioning solution have limited its application for long-term positioning solution. However, it can play a key role in those applications where tri-axial attitude and accelerations can be indicators of curves, slopes, cants, etc. and it can be used as diagnostic of other sensors measurements. This work is focused on the use of IMUs for diagnostic applications and it compares medium-end (xSens MTi-100) and low-end (Bosch BMI160) grade MEMS-based IMUs in an experimental road test using a fusion of a high-end IMU (KVH GEO-FOG), GNSS and wheel speed sensor as reference. In addition, a research question about whether the calibration is the main reason between different grade IMUs has been formulated. Thus, a simple, manufacturable and cost-efficient calibration technique is applied to the low-end IMU in order to compare its performance improvement with the medium-end one. The raw measurements (angular rates and specific forces) and navigation states (tri-axial attitude and accelerations) are considered for diagnosis and they are statistically compared to evaluate the performance of each IMU. It is concluded that the calibration technique used makes the low-end IMU performance similar to that of the medium-end one. Consequently, this work contributes to optimizing the cost of land vehicular positioning systems when choosing the most appropriate sensor based on the accuracy and precision required for the application.

Performance Evaluation of Different Grade IMUs for Diagnosis Applications in Land Vehicular Multi-Sensor Architectures

Indoor localization using magnetic and inertial sensors has shown its effectiveness due to the pervasiveness of magnetic fields and independence from external infrastructure. However, when massive crowdsourced data are obtained in mass-market applications, new issues occur and degrade the magnetic matching (MM) performance. To alleviate these issues, an orientation-aided stochastic MM method is presented. First, orientation-aided magnetic fingerprints are applied to effectively improve fingerprint fidelity when massive data are used. Second, this paper characterizes crowdsourced magnetic fingerprints and reveals that the stochastic magnetic components may have different histograms, such as the symmetric, left-skewed, bimodal, and irregular ones. Based on such outcome, Gaussian distribution and histogram model based stochastic MM methods are presented to mitigate the degradation from stochastic magnetic components, which have not been involved in the existing MM methods such as nearest neighbor and dynamic time warping. Compared to the deterministic MM methods, this research provides a deeper insight into the use of magnetic fingerprints and more accurate MM and MM/dead-reckoning integrated localization solutions.

Orientation-Aided Stochastic Magnetic Matching for Indoor Localization

One of the important factors leading to traffic accidents is the abnormal driving behavior of drivers. Early detection of abnormal driving behaviors can effectively reduce the occurrence of traffic accidents. At present, most of the mainstream driving behavior detection methods are based on the data of a single moment, which separates the continuity of driving behavior. In this article, a driving behavior recognition algorithm based on Serial-Feature Network (SF-Net) and smart phone inertial sensor is proposed, which fully considers the continuity of driving events and uses adjacent multi time data to identify driving status. The data used in this article are collected from GPS data, 3-axis acceleration and gyroscope data of smart phone. Through the preprocessing operation, SF-net makes the input vector not only contain the current sensor data, but also fuse the relevant information of adjacent time. In SF-net, deep convolution neural network is used for feature extracting, and 10 different driving behaviors can be identified by fusing multi-level and multi-time feature information. The field test results show that the accuracy rate of the serial feature network is 97.1%, and the recall rate is 98.4%, which is better than other test network models. When the number of training samples is small, the sequential feature network can still maintain a high recognition rate, and the network model is relatively stable.

Smartphone Sensors-Based Abnormal Driving Behaviors Detection: Serial-Feature Network

In this paper, a low-cost attitude estimation system (AES) is designed to estimate the attitude information of the vehicle utilizing MEMS inertial measurement unit (MIMU) and single baseline global positioning system (GPS). The proposed AES adopts a nonlinear attitude observer to fuse different sensors and estimate the vehicle’s attitude. With the help of single baseline GPS, a maneuver accelerations compensation algorithm is used to improve the accuracy of the attitude angles and gyro biases. Modified quaternions are also used for the attitude observer to separate the yaw angle from the gravity angles easily when the single baseline GPS information is absent. The proposed AES judges the maneuver state of the vehicle depending on a switch criteria and adjusts the gain of the observer during GPS outages. The rules can efficiently detect the maneuver state of the vehicle and make full use of the inertial sensor outputs than classical rules. The experimental tests show that the proposed AES has a good attitude estimation accuracy, which meets the requirements of the Satcom-on-the-Move (SOTM) systems.

A Nonlinear Observer for Attitude Estimation of Vehicle-Mounted Satcom-on-the-Move

A novel pseudo-monopulse tracking (PMT) method is presented for Ku-band Satcom-on-the-move (SOTM). An antenna architecture consisting of multiple distributed panels capable of moving back and forth mechanically is introduced firstly, which can effectively reduce the overall height. Two independent beams with hybrid beam steering approach are introduced to keep the excellent performance in terms of tracking accuracy and antenna gain over wide scan coverage. Then, a hybrid compensation method using switched delay lines and phase shifters is applied to solve the frequency scan effect. Finally, the tracking beam is sequentially scanned at a prescribed offset angle in different directions, and the pointing error is calculated by comparing the corresponding received beacon signal strengths. Simulation results confirm the veracity of the proposed tracking method.

Pseudo-monopulse tracking method for low profile mobile satellite antenna system

The invention discloses a satellite communication in motion rapid capturing method; the method comprises the following steps of 1, performing system initializing; 2, establishing a coordinate system;3, carrying out pre-adjustment of antenna pointing; 4, carrying out coarse capture of an antenna; and 5, carrying out accurate alignment of the antenna. The method is simple in steps, reasonable in design and convenient to implement, and pre-adjustment of antenna pointing is realized by utilizing the position and speed information of a carrier detected by a GPS module, so that the target communication satellite can be rapidly captured under different motion states of the carrier, the accurate alignment of the antenna and the target communication satellite is realized, the capture and fine alignment time is short, the stability of the antenna relative to the inertial space is ensured, the rapid response capability of the communication in motion system is improved, and the method is high inpracticability.

Ruihua Chang

Papers

A Nonlinear Observer for Attitude Estimation of Vehicle-Mounted Satcom-on-the-Move

Pseudo-monopulse tracking method for low profile mobile satellite antenna system

Satellite communication in motion rapid capturing method

Square-Root Divided Difference Attitude Filter for SOTM Using Low Cost Sensors