An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

Amirzadeh, Ahmadreza; Karimpour, Ali

doi:10.1016/j.dsp.2013.05.002

Cited by 18 publications

(7 citation statements)

References 30 publications

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“…The entire sensor is mounted on a tripod beside the computer. In this paper, we adopt the Kalman filter to track the target [ 24 ]. The designed cameras can converge and focus on moving targets automatically based on the measured distance.…”

Section: Auto-converged Camera Array Realizationmentioning

confidence: 99%

Analysis of Camera Arrays Applicable to the Internet of Things

Yang

et al. 2016

Sensors

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The Internet of Things is built based on various sensors and networks. Sensors for stereo capture are essential for acquiring information and have been applied in different fields. In this paper, we focus on the camera modeling and analysis, which is very important for stereo display and helps with viewing. We model two kinds of cameras, a parallel and a converged one, and analyze the difference between them in vertical and horizontal parallax. Even though different kinds of camera arrays are used in various applications and analyzed in the research work, there are few discussions on the comparison of them. Therefore, we make a detailed analysis about their performance over different shooting distances. From our analysis, we find that the threshold of shooting distance for converged cameras is 7 m. In addition, we design a camera array in our work that can be used as a parallel camera array, as well as a converged camera array and take some images and videos with it to identify the threshold.

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Section: Auto-converged Camera Array Realizationmentioning

confidence: 99%

Analysis of Camera Arrays Applicable to the Internet of Things

Yang

et al. 2016

Sensors

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“…When d = 0, at this point the measurement noise is not needed to be adjusted; when d < 1, the adjustment range is small and the cycle is longer, but the process is stable; when d > 1, the adjustment range is larger and cycle is shorter, but it is easier to generate oscillation. c k can be obtained by fuzzy inference system (FIS) [21] and input reference of FIS is got by the difference between residual observed value and estimate value with INS/UWB measurement model. Defining r as the measurement residual, T r as measurement variance, and V r as estimating equations, combined with (23), we can get

\begin{matrix} r_{k} = Z_{k} (t) - {\hat{Z}}_{k} (t) = Z_{k} (t) - H_{k} {\hat{X}}_{k ∣ k - 1}, \end{matrix}

\begin{matrix} T_{r} = \frac{1}{N} {false\sum}_{i = i_{normal0}}^{k} r_{i} r_{i}^{T}, \end{matrix}

\begin{matrix} V_{r} = M_{k - 1} + H_{k} (Φ_{k, k - 1} P_{k - 1} Φ_{k, k - 1}^{T} + Q) H_{k}^{T} . \end{matrix}

…”

Section: Optimal Comprehensive and Filtering Strategy Of Ins/uwbmentioning

confidence: 99%

Integrated Navigation Fusion Strategy of INS/UWB for Indoor Carrier Attitude Angle and Position Synchronous Tracking

Fan

Jing

et al. 2014

The Scientific World Journal

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In some GPS failure conditions, positioning for mobile target is difficult. This paper proposed a new method based on INS/UWB for attitude angle and position synchronous tracking of indoor carrier. Firstly, error model of INS/UWB integrated system is built, including error equation of INS and UWB. And combined filtering model of INS/UWB is researched. Simulation results show that the two subsystems are complementary. Secondly, integrated navigation data fusion strategy of INS/UWB based on Kalman filtering theory is proposed. Simulation results show that FAKF method is better than the conventional Kalman filtering. Finally, an indoor experiment platform is established to verify the integrated navigation theory of INS/UWB, which is geared to the needs of coal mine working environment. Static and dynamic positioning results show that the INS/UWB integrated navigation system is stable and real-time, positioning precision meets the requirements of working condition and is better than any independent subsystem.

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“…Kalman algorithm and fuzzy logic have also been linked in [9] by Amirzadeh and Karimpour, in which they considered an interacting fuzzy fading memory based augmented Kalman filter for target tracking during maneuvers. Their solution solved the problem of unknown target acceleration.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Trajectory Tracking Using Radar Equipment with Fuzzy Logic Algorithm

2020

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Radio-electronic means, including equipment for transmissions, radio-location, broadcasting, and navigation, allow the execution of various research missions and combat forces management. Determining the target coordinates and directing the armament towards them, obtaining and processing data about enemies, ensuring the navigation of ships, planes and outer atmospheric means, transmitting orders, decisions, reports and other necessary information for the armed forces; these are only some of the possibilities of radio-electronic technology. Fuzzy logic allows the linguistic description of the laws of command, operation and control of a system. When working with complex and nonlinear systems, it can often be observed that, as their complexity increases, there is a decrease in the significance of the details in describing the global behavior of the system. Even though such an approach may seem inadequate, it is often superior and less laborious than a rigorous mathematical approach. The main argument in favor of fuzzy set theory is to excel in operating with imprecise, vague notions. This article demonstrates the superiority of a fuzzy tracking system over the standard Kalman filter tracking system under the conditions of uneven accelerations and sudden change of direction of the targets, as well as in the case of failure to observe the target during successive scans. A cascading Kalman filtering algorithm was used to solve the speed ambiguity and to reduce the measurement error in real-time radar processing. The cascade filters are extended Kalman filters with controlled gain using fuzzy logic for tracking targets using radar equipment under difficult tracking conditions.

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An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

Cited by 18 publications

References 30 publications

Analysis of Camera Arrays Applicable to the Internet of Things

Analysis of Camera Arrays Applicable to the Internet of Things

Integrated Navigation Fusion Strategy of INS/UWB for Indoor Carrier Attitude Angle and Position Synchronous Tracking

Aircraft Trajectory Tracking Using Radar Equipment with Fuzzy Logic Algorithm

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