A Dead-Reckoning Positioning Scheme Using Inertial Sensing for Location Estimation

Chiou, Yih Shyh; Tsai, Fuan; Yeh, Sheng Cheng

doi:10.1109/iih-msp.2014.100

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…Wireless communication technology makes KF usability greatly improved. Localization technologies have received high attention for LBS and IoT applications recently [21][22][23][24][25][26][27][28][29]. KF is also known as the optimal linear filter and is called the linear combination of the output value.…”

Section: Kalman Filtering Algorithmmentioning

confidence: 99%

“…Referring to several references [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], the proposed filtering algorithm is designed KF has very complex matrix inversion and matrix calculation, and this algorithm can greatly reduce the complexity of the calculation. A proposed tracking approach is based on the KF approach with steady state.…”

Section: The Proposed Filtering Algorithm Based On Chipmentioning

confidence: 99%

“…Under the environment with larger KG, use PKF, as shown in Equation (21). Conversely, if you encounter environment with smaller KG, use DKF, as shown in Equation (22). Selecting the appropriate judgment equation according to different environments can greatly reduce the tracking error and thus obtain more accurate position results.…”

Section: Dkf and Pkf Filtering Algorithmmentioning

confidence: 99%

“…However, software implementations often result in speed delay and cannot provide in-time positioning information for LBS applications [17][18][19][20]. In order to improve the computational speed of positioning systems and accuracy, there already are lots of improved algorithms in literature [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. The calculations of the KF approach are involved in the number of matrix computations, which include addition, multiplication, and inverse operations.…”

Section: Introductionmentioning

confidence: 99%

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Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation

Chiou

Chen

2023

Electronics

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This article puts forward a low-complexity filtering algorithm to achieve a low-complexity filtering chip design for real-time location tracking. In order to meet the need for low-complexity and real-time, the positioning tracking algorithm based on Kalman Filter (KF) is proposed. The KF itself has the functions of tracking, predicting, etc., which can correct the positioning into more accurate results. However, in the calculation of KF algorithms, each iteration often requires tedious and complex calculations of Kalman Gain (KG). Both software and hardware are very resource-intensive. Therefore, use the feature of KG in alpha-beta (α-β) filtering algorithm which can gradually balance in each iteration. Proposed a filtering algorithm that is based on low-complexity, low cost, and high efficiency. This algorithm uses DKF (Difference Kalman Filter) and PKF (Percentage Kalman Filter) depending on different environments. In other words, DKF and PKF are the algorithms which are generated based on different judging conditions. This algorithm can not only significantly reduce the time and the complexity of computing, but also greatly shorten the circuit area of the original algorithm. This algorithm has a large number of matrix operation. In the hardware calculation process, it solves matrix problems about hardware and then developed chip design. Coefficients are used by a multiple of 2 for operation. Use shifters instead of multipliers and dividers, significantly reducing complexity and circuit area. At the same time, deal with the problem of a floating-point number, achieve circuit function verification on the FPGA, and finally tape-out. The design uses the TSMC 0.18μm CMOS cell library provided by the TSRI, uses EDA to implement VLSI with the Design Vision of SYNOPSYS, the operating frequency of the circuit is 83.33 MHz, the value of gate counts is 22.84 K, the power consumption is 3.86 mW, and chip area is 582.63 μm × 580.23 μm.

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Section: Kalman Filtering Algorithmmentioning

confidence: 99%

Section: The Proposed Filtering Algorithm Based On Chipmentioning

confidence: 99%

Section: Dkf and Pkf Filtering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation

Chiou

Chen

2023

Electronics

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“…Chio et al, [19] presents a dead-reckoning (DR) positioning approach based on inertialmeasurement-unit (IMU) and improving the accuracy of the position using Character Recognition algorithm by extracting road names of street signs.…”

Section: State Of the Artmentioning

confidence: 99%

An Event-Based Messaging Architecture for Vehicular Internet of Things (IoT) Platforms

Aravind

Wiklander

Palmheden

et al. 2017

ICT Innovations 2017

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Internet of Things (IoT) has revolutionized transportation systems by connecting vehicles consequently enabling their tracking, as well as monitoring of driver activities. The IoT platform for most vehicles typically consists of 1) an on-board system consisting of the communication unit, sensors and a set of ECUs that are interconnected using a CAN network, 2) an off-board system consisting of the applications deployed on the servers (e.g., cloud) that processes the data send by the communication unit over the internet, and 3) mobile devices like a mobile phone or a computer that communicates with the on-board and off-board systems. Such an IoT platform requires a significant amount of data to be send from the on-board system to the off-board servers, contributing to high network usage.There are two main architectural paradigms for sending data: 1) interval based architecture, in which data is send at regular intervals and 2) event based architecture, in which data is send whenever relevant events occur. Currently, (e.g., at Scania), the data is being send at regular intervals, i.e., using an interval based approach. In this case, data is send even if it is not relevant for reporting leading to a wastage of network resources, e.g., when the data does not change considerably compared to the previously sent value. Sending data in an event-based manner, when the data is relevant for reporting, e.g., changes significantly, reduces the network usage when compared to the interval based approach.In this thesis, we investigate the possibility of using an event based architecture to send data from the on-board system to the off-board system in order to reduce network usage and improve the accuracy of the data available off-board. We first propose an event based architecture for data transfer in the context of Internet of vehicles. We then implement a simulator to evaluate our proposed architecture for the specific case of position data. Finally, we perform extensive experiments varying different parameters and compare, for example, average message size per minute and average off-board error distance.The results show that our event based architecture improves the accuracy of data available at the off-board system, by a careful selection of events. Moreover, we found that our event based architecture significantly decreases the frequency of sending messages, particularly during highway driving, leading to reduced average data transfer rates. Our results enable a customer to perform trade-offs between accuracy and data transfer rates.Future work will aim at implementing the event based architecture on a real platform as well as investigating the possibility of using the event based architecture for more accurate prediction by incorporating additional details such as the final destination of the vehicle and odometer values.

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FPGA-Based Implementation of Reduced-Complexity Filtering Algorithm for Real-Time Location Tracking

Chen

Chiou

et al. 2019

2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf

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A Dead-Reckoning Positioning Scheme Using Inertial Sensing for Location Estimation

Cited by 6 publications

References 11 publications

Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation

Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation

An Event-Based Messaging Architecture for Vehicular Internet of Things (IoT) Platforms

FPGA-Based Implementation of Reduced-Complexity Filtering Algorithm for Real-Time Location Tracking

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