Global Positioning System (GPS): A Low-Cost Velocity Sensor for Correcting Inertial Sensor Errors on Ground Vehicles

2004 ◽  
Vol 126 (2) ◽  
pp. 255-264 ◽  
Author(s):  
David M. Bevly

This paper demonstrates the ability of a standard low-cost Global Positioning System (GPS) receiver to reduce errors inherent in low-cost accelerometers and rate gyroscopes used on ground vehicles. Specifically GPS velocity is used to obtain vehicle course, velocity, and road grade, as well as to correct inertial sensors errors, providing accurate longitudinal and lateral acceleration, and pitch, roll, and yaw angular velocities. Additionally, it is shown that transient changes in sideslip (or lateral velocity), roll, and pitch angles can be measured. The method utilizes GPS velocity measurements to determine the inertial sensor errors using a kinematic Kalman Filter estimator. Simple models of the inertial sensors, which take into account the sensor noise and bias drift properties, are developed and used to design the estimator. Based on the characteristics of low-cost GPS receivers and IMU sensors, this paper presents the achievable performance of the combined system using the covariance analysis from the Kalman filter. Subsequent simulations and experiments validate both the error analysis and the methodology for utilizing GPS as a velocity sensor for correcting low-cost inertial sensor errors and providing critical vehicle state measurements.

2011 ◽  
Vol 44 (1) ◽  
pp. 10746-10751
Author(s):  
Vasko Sazdovski ◽  
Mile Stankovski ◽  
Tatjana Kolemisevska Gugulovska ◽  
Stojce Deskovski

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Matthew Cossaboom ◽  
Jacques Georgy ◽  
Tashfeen Karamat ◽  
Aboelmagd Noureldin

Owing to their complimentary characteristics, global positioning system (GPS) and inertial navigation system (INS) are integrated, traditionally through Kalman filter (KF), to obtain improved navigational solution. To reduce the overall cost of the system, microelectromechanical system- (MEMS-) based INS is utilized. One of the approaches is to reduce the number of low-cost inertial sensors, decreasing their error contribution which leads to a reduced inertial sensor system (RISS). This paper uses KF to integrate GPS and 3D RISS in a loosely coupled fashion to enhance navigational solution while further improvement is achieved by augmenting it with map matching (MM). The 3D RISS consists of only one gyroscope and two accelerometers along with the vehicle’s built-in odometer. MM limits the error growth during GPS outages by restricting the predicted positions to the road networks. The performance of proposed method is compared with KF-only 3D RISS/GPS integration to demonstrate the efficacy of the proposed technique.


2014 ◽  
Vol 68 (2) ◽  
pp. 355-366 ◽  
Author(s):  
Burak H. Kaygısız ◽  
Bekir Şen

This paper presents a new type of Global Positioning System/Inertial Navigation System (GPS/INS) providing higher navigation accuracy under large initial heading error. The mechanization introduced is applicable to low cost GPS/INS systems and enhances the performance when the heading error is large. The proposed approach has the capability to decrease large heading errors very quickly and can start the strapdown navigation computations under poor heading accuracy without any special alignment procedure. Although the design is applicable to land, sea and aerial vehicles, a land vehicle is used for the performance tests. The test is conducted around a closed path and the proposed system is compared to a GPS/INS system based on small attitude error assumption. The performance of both systems is given in this paper.


Recently, indoor localization has witnessed an increase in interest, due to the potential wide range of using in different applications, such as Internet of Things (IoT). It is also providing a solution for the absence of Global Positioning System (GPS) signals inside buildings. Different techniques have been used for performing the indoor localization, such as sensors and wireless technologies. In this paper, an indoor localization and object tracking system is proposed based on WiFi transmission technique. It is done by distributing different WiFi sources around the building to read the data of the tracked objects. This is to measure the distance between the WiFi receiver and the object to allocate and track it efficiently. The test results show that the proposed system is working in an efficient way with low cost.


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