A fuzz application in GPS/INS navigation

Miao, Zhibin; Zhang, Hongtian; Zhang, Jinzhu; Geng, Ruiguang

doi:10.1109/iccasm.2010.5619104

Cited by 1 publication

(3 citation statements)

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“…The objective fuzzy system’s structure is illustrated in Figure 1 . The objective fuzzy logic system and subtractive clustering method can be described as the following [ 9 , 35 ].…”

Section: Objective Fuzzy Logic System and Subtractive Clustering Mmentioning

confidence: 99%

“…The pseudo-inverse solving method is well-known for this question. That is,

gives the minimal value of

[ 35 ].…”

Section: Objective Fuzzy Logic System and Subtractive Clustering Mmentioning

confidence: 99%

“…In order to discover the optimal four values (

is accept ratio,

is cluster radius,

is reject ratio,

is squash factor) should make less for the errors. A first-order Sugeno modeling method is used, which employing LSE approaches optimizes the values [ 35 ]. The range of

is [0 2], the range of

is [0 1], the range of

is [0 1], and the range of

is [0 1].…”

Section: Objective Fuzzy Logic System and Subtractive Clustering Mmentioning

confidence: 99%

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An Accurate and Generic Testing Approach to Vehicle Stability Parameters Based on GPS and INS

Miao

Zhang

2015

Sensors

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With the development of the vehicle industry, controlling stability has become more and more important. Techniques of evaluating vehicle stability are in high demand. As a common method, usually GPS sensors and INS sensors are applied to measure vehicle stability parameters by fusing data from the two system sensors. Although prior model parameters should be recognized in a Kalman filter, it is usually used to fuse data from multi-sensors. In this paper, a robust, intelligent and precise method to the measurement of vehicle stability is proposed. First, a fuzzy interpolation method is proposed, along with a four-wheel vehicle dynamic model. Second, a two-stage Kalman filter, which fuses the data from GPS and INS, is established. Next, this approach is applied to a case study vehicle to measure yaw rate and sideslip angle. The results show the advantages of the approach. Finally, a simulation and real experiment is made to verify the advantages of this approach. The experimental results showed the merits of this method for measuring vehicle stability, and the approach can meet the design requirements of a vehicle stability controller.

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“…The objective fuzzy system’s structure is illustrated in Figure 1 . The objective fuzzy logic system and subtractive clustering method can be described as the following [ 9 , 35 ].…”

Section: Objective Fuzzy Logic System and Subtractive Clustering Mmentioning

confidence: 99%

“…The pseudo-inverse solving method is well-known for this question. That is,

gives the minimal value of

[ 35 ].…”

Section: Objective Fuzzy Logic System and Subtractive Clustering Mmentioning

confidence: 99%

“…In order to discover the optimal four values (

is accept ratio,

is cluster radius,

is reject ratio,

is squash factor) should make less for the errors. A first-order Sugeno modeling method is used, which employing LSE approaches optimizes the values [ 35 ]. The range of

is [0 2], the range of

is [0 1], the range of

is [0 1], and the range of

is [0 1].…”

Section: Objective Fuzzy Logic System and Subtractive Clustering Mmentioning

confidence: 99%

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