<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">The kinematic filter is a common tool in control and signal processing applications dealing with position, velocity and other kinematical variables. Usually the filter gain is given a fixed value determined due to dynamic and measurement conditions. Most studies provide analytical solutions for optimal gains in particular scenarios. In practice, due to a lack of information (or under timevarying conditions) these recipes are mostly inapplicable and the kinematic filter requires appropriate adaptation tools instead. In its simplest form, the problem may be formulated as the gain adaptation under the tracking index uncertainty. We suggest a simple adaptive-gain kinematic filter based on minimization of the innovation variance which is known to give the optimal Kalman gain. The study deals with commonly used kinematic models of order 2-4. As shown, for any order of the kinematic filter its transfer function matches the moving-averaging (MA) model parameterized by the filter gain. In this view, the adaptive kinematic filter may be implemented in a variety of forms either based on the MA identification or by a direct gain adaptation. Optimal closed-form solutions may be incorporated into the adaptive filter as constraints. With the optimally constrained gain-vector components, the multipleparameter adaptive filter is translated into a beneficial single-parameter version. The simulation study demonstrates behavior of suggested filters in a wide range of conditions.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>
The so-called adaptive-gain-and-tau filter extends a recently suggested adaptive-gain tracking filter to a case with correlated target maneuver. The transition matrix of an appropriate Kalman filter (KF) depends on the maneuver correlation time, and the associated with this KF transfer function specifies the ARMA coefficients coupling the correlation time and the KF gain. Due to this link, one adaptation scheme is based on the ARMA identification. Another form of the adaptive filter represents a joint parameter/state estimator. Simulations undertaken for tracking filters of orders 2-4 illustrate their behavior and compare adaptively found parameters to optimal.
Range tracking, a traditional part of the radar/sonar range gating technique, requires a fully adaptive however simple target predictor. Under the constant velocity (CV) assumption, one can apply an adaptive gain form of the α-β filter based on the recursive prediction error method. The adaptive α-β terms tend to the Kalman gain and remain nearly optimal with a minor degradation caused by the parameter misadjustment noise. An extended variant of the filter allows, in addition to the CV form, both the polynomial and stochastic signals. The adaptive tracker improves the sensor bandwidth and allows more accurate range gating.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.