Adaptive Kalman filter for tracking maneuvering targets

Bekir, Esmat

doi:10.2514/3.19852

Cited by 33 publications

(7 citation statements)

References 2 publications

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“…The Kalman filter is a set of mathematical equations that provides an efficient recursive computational means to estimate the state of a process, in such a way that minimizes the mean of the squared error. The filter is very useful as it supports estimations of past, present and even future states and it can do so even when the precise nature of the modeled system is unknown [19]. The Kalman filter requires an exact knowledge of the process noise covariance Q and the measurement noise covariance R that depends on the sensor measurements.…”

Section: Kalman Filter With Recursive Least Square Algorithmmentioning

confidence: 99%

Groundwater contaminant boundary input flux estimation in a two-dimensional aquifer

Nauman

Kim

Huang

et al. 2010

Journal of Industrial and Engineering Chemistry

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Section: Kalman Filter With Recursive Least Square Algorithmmentioning

confidence: 99%

Groundwater contaminant boundary input flux estimation in a two-dimensional aquifer

Nauman

Kim

Huang

et al. 2010

Journal of Industrial and Engineering Chemistry

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“…Since the complete mathematical details of the algorithm are well described in [2] and [3], they will not be repeated here. The basic notion of an algorithm of this type is to monitor the Kalman filter innovations process and detect the presence of a target maneuver based on the sum of the innovations computed over a sliding window, whose length can be optimally determined based on the assumed false-alarm rate.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…Since the innovation-based process noise modification techniques seem to be less computationally intensive and seem to be well-suited for application on board an interceptor, an algorithm of this type was chosen for implementation. The particular algorithm that will be used is completely described in [2] and [3].…”

Section: Introductionmentioning

confidence: 99%

“…There are three broad classes of adaptive filtering algorithms which have been used to improve the tracking of maneuvering targets. The first class involves modification of the filter process noise upon detection of the target maneuver [2][3][4][5][6][7]. This modification may be either continuous or discrete, where several levels of noise can be assumed for the filter, with a switching mechanism provided.…”

Section: Introductionmentioning

confidence: 99%

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Application of adaptive filtering techniques to interceptors

Hill¹

1996

Guidance, Navigation, and Control Conference

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An interceptor may encounter highly maneuverable targets that may cause its on-board filter to lose tracking of the target, thereby causing the interceptor to miss the target. These tracking filters typically estimate either line-of-sight/line-of-sight rate or position/velocity (depending on the type of measurements available) for guidance purposes and are usually constant-parameter Kalman filters. Here, we demonstrate the applicability of a particular adaptive filtering technique which monitors the innovations process to detect the presence of a maneuver and subsequently modifies the filter process noise term. This particular technique is less computationally intensive than other adaptive filtering techniques and seems to be well suited for implementation on board an interceptor. We then implement and test the algorithm in a 6-degree-of-freedom terminal homing simulation of both interceptor and target and demonstrate improved tracking performance and miss distance using the adaptive filter for a limited set of accelerating targets. (Author) Abstract An interceptor may encounter highly maneuverable targets that may cause its on-board filter to lose tracking of the target, thereby causing the interceptor to miss the target. These tracking filters typically estimate either line-of-sight/line-of-sight rate or position/velocity (depending on the type of measurements available) for guidance purposes and are usually constant-parameter Kalman filters. There has been a significant amount of research performed recently in the area of adaptive filtering. In this effort, we will demonstrate the applicability of a particular adaptive filtering technique which monitors the innovations process to detect the presence of a maneuver and subsequently modifies the filter process noise term.This particular technique is less computationally intensive than other adaptive filtering techniques and seems to be well-suited for implementation on board an interceptor. We will then implement and test the algorithm in a 6 Degree of Freedom terminal homing simulation of both interceptor and target and will demonstrate improved tracking performance and miss distance using the adaptive filter for a limited set of accelerating targets.

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“…Conventional adaptive filters such as the adaptive Kalman filter can be applied to maneuvering target tracking . In this adaptive filter, two additional schemes exist beyond the standard Kalman filtering, ie, maneuvering detection and adjusting the state noise variance.…”

Section: Introductionmentioning

confidence: 99%

Minimax particle filtering for tracking a highly maneuvering target

Lim

Kim

Park

2019

Intl J Robust & Nonlinear

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Summary In this paper, we propose a new framework of particle filtering that adopts the minimax strategy. In the approach, we minimize a maximized risk, and the process of the risk maximization is reflected when computing the weights of particles. This scheme results in the significantly reduced variance of the weights of particles that enables the robustness against the degeneracy problem, and we can obtain improved quality of particles. The proposed approach is robust against environmentally adverse scenarios, particularly when the state of a target is highly maneuvering. Furthermore, we can reduce the computational complexity by avoiding the computation of a complex joint probability density function. We investigate the new method by comparing its performance to that of standard particle filtering and verify its effectiveness through experiments. The employed strategy can be adopted for any other variants of particle filtering to enhance tracking performance.

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Adaptive Kalman filter for tracking maneuvering targets

Cited by 33 publications

References 2 publications

Groundwater contaminant boundary input flux estimation in a two-dimensional aquifer

Groundwater contaminant boundary input flux estimation in a two-dimensional aquifer

Application of adaptive filtering techniques to interceptors

Minimax particle filtering for tracking a highly maneuvering target

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