A General Solution to Optimal Fixed-Gain (&lt;formula formulatype="inline"&gt;&lt;tex Notation="TeX"&gt;$\alpha$&lt;/tex&gt;&lt;/formula&gt;-&lt;formula formulatype="inline"&gt;&lt;tex Notation="TeX"&gt;$\beta$&lt;/tex&gt;&lt;/formula&gt;-&lt;formula formulatype="inline"&gt;&lt;tex Notation="TeX"&gt;$\gamma$&lt;/tex&gt;&lt;/formula&gt; etc.) Filters

Crouse, David Frederic

doi:10.1109/lsp.2014.2376876

Cited by 22 publications

(2 citation statements)

References 24 publications

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“…Moving object tracking obtains accurate and sequential estimation of the target position and velocity by using Eqs. (9)- (13). As indicated in Eqs.…”

Section: Aspects Of Tracking Filter Designmentioning

confidence: 99%

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Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Saho¹

2018

Kalman Filters - Theory for Advanced Applications

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This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.

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“…Moving object tracking obtains accurate and sequential estimation of the target position and velocity by using Eqs. (9)- (13). As indicated in Eqs.…”

Section: Aspects Of Tracking Filter Designmentioning

confidence: 99%

“…In the years since, further research on these issues has been conducted, but no satisfactory solutions to the abovementioned problems have been presented. Crouse [13] described a general solution for optimal trackers in a steady state. However, this method also requires an empirical selection of the dynamic models.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Saho¹

2018

Kalman Filters - Theory for Advanced Applications

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Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking

Nenavath

Jatoth

2018

Neural Comput & Applic

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Nonlinear model predictive torque control of PMSMs for high performance applications

Englert

Graichen

2018

Control Engineering Practice

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A General Solution to Optimal Fixed-Gain (<formula formulatype="inline"><tex Notation="TeX">$\alpha$</tex></formula>-<formula formulatype="inline"><tex Notation="TeX">$\beta$</tex></formula>-<formula formulatype="inline"><tex Notation="TeX">$\gamma$</tex></formula> etc.) Filters

Cited by 22 publications

References 24 publications

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Kalman Filter for Moving Object Tracking: Performance Analysis and Filter Design

Hybrid SCA–TLBO: a novel optimization algorithm for global optimization and visual tracking

Nonlinear model predictive torque control of PMSMs for high performance applications

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