This paper proposes an attack intent inference framework for defending against hypersonic glide vehicles (HGVs). Predicting the HGV behaviors poses significant challenges for defense systems due to their highly dynamic and erratic maneuvers. Complementing the limitations of the dynamics model, a unified dynamics and decision-making model of HGV is developed. First, dynamically feasible attack regions can be set by the dynamics model. Within this region, the decision-making model encodes the rational intent of attack, strategically selecting the target that maximally attains the threat value. To further address the dynamical uncertainties and potential discrepancies from the rational decision-making model, a proximity parameter is introduced in light of the Maximum Entropy principle. The attack intent of the HGV is then inferred by the Bayesian approach, whereby recursively updates the probability of the potential target to be attacked. Numerical simulations demonstrate that the proposed framework achieves superior accuracy and faster convergence in intent inference compared to existing methods, under different scenarios with varying uncertainty levels.

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch’s specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code.

A precise range difference (RD)-based passive target localization algorithm is proposed for mobile robot applications. To effectively solve the real-time issue, the nonlinear relation between the RD information and the target location is removed by introducing the target range as an auxiliary variable to be estimated. Then, the RD-based localization problem is formulated in the setting of linear estimation. The resultant linear measurement model contains the stochastic parametric uncertainty which causes the severe performance degradation of the conventional linear least squares (LS) method when the RD measurement noise is not negligible. To cope with this problem, the recently developed linear robust LS (RoLS) estimation theory is applied for the passive target localization problem. Using the geometric relation among the ultrasonic receivers, a systematic way to determine the design parameters of the RoLS estimator is suggested. It is shown that the proposed method can provide the nearly unbiased target location estimates for the whole location area. The proposed solution is very practical because it is preferable for real-time robot applications owing to its linear recursive structure. Through the computer simulations and actual experiments, it is shown that the proposed algorithm guarantees the superior localization performance and the fast convergence compared to the existing one.

This paper proposes an attack intent inference framework for defending against hypersonic glide vehicles (HGVs). Predicting the HGV behaviors poses significant challenges for defense systems due to their highly dynamic and erratic maneuvers. Complementing the limitations of the dynamics model, a unified dynamics and decision-making model of HGV is developed. First, dynamically feasible attack regions can be set by the dynamics model. Within this region, the decision-making model encodes the rational intent of attack, strategically selecting the target that maximally attains the threat value. To further address the dynamical uncertainties and potential discrepancies from the rational decision-making model, a proximity parameter is introduced in light of the Maximum Entropy principle. The attack intent of the HGV is then inferred by the Bayesian approach, whereby recursively updates the probability of the potential target to be attacked. Numerical simulations demonstrate that the proposed framework achieves superior accuracy and faster convergence in intent inference compared to existing methods, under different scenarios with varying uncertainty levels.

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch’s specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code.

A precise range difference (RD)-based passive target localization algorithm is proposed for mobile robot applications. To effectively solve the real-time issue, the nonlinear relation between the RD information and the target location is removed by introducing the target range as an auxiliary variable to be estimated. Then, the RD-based localization problem is formulated in the setting of linear estimation. The resultant linear measurement model contains the stochastic parametric uncertainty which causes the severe performance degradation of the conventional linear least squares (LS) method when the RD measurement noise is not negligible. To cope with this problem, the recently developed linear robust LS (RoLS) estimation theory is applied for the passive target localization problem. Using the geometric relation among the ultrasonic receivers, a systematic way to determine the design parameters of the RoLS estimator is suggested. It is shown that the proposed method can provide the nearly unbiased target location estimates for the whole location area. The proposed solution is very practical because it is preferable for real-time robot applications owing to its linear recursive structure. Through the computer simulations and actual experiments, it is shown that the proposed algorithm guarantees the superior localization performance and the fast convergence compared to the existing one.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> An improved pinch detection algorithm is proposed for low-cost antipinch window control systems. Apart from previous works, the proposed algorithm makes use of torque rate information to sense pinched conditions and to perform safety precautions. The motivation for this approach comes from the idea that the torque rate is less sensitive to motor parameter uncertainty than the torque or the angular velocity. The pinch estimator is designed by applying steady-state Kalman filter recursion to the augmented system model which includes the motor dynamics model and an additional torque rate state. The external torque rate is estimated using angular velocity measurements calculated from the Hall sensor output. A systematic way to set a reasonable threshold of the torque rate estimates under pinched conditions is suggested through deterministic estimation error analysis. Therefore, the proposed algorithm is able to prevent performance degradation due to the empirical threshold level as well as due to motor parameter variations. Experimental results show that our method satisfies EU legal requirements and guarantees robustness against parametric uncertainties. </para>

This paper presents a method that analyzes long-term position errors in the SDINS(strap-down inertial navigation system) for underwater vehicle applications. Under the assumption that the initial platform misalignment and the inertial sensor biases are the primary sources of the position errors in the SDINS, an analytic solution is derived based on the error propagation model in which these factors serve as the state variables. By using the vertical channel damping loop within the SDINS, the analytic solution is obtained by focusing on the position error in the horizontal plane. The proposed method enables a mathematical analysis of the SDINS position errors with respect to each error state, observing the oscillation terms such as the Schuler/Foucault frequency and the Earth’s rotation. Thus, the proposed solution is expected to be used as a theoretical basis for the development of the underwater vehicles’ navigation system. The effectiveness of the proposed solution is verified by the results of SDINS position error through computer simulations.

This study proposes a new linear state estimation filter for radar target tracking. Unlike the conventional converted measurement technique, target motion and radar measurements are modeled in mixed Cartesian coordinate systems. Considering the coordinate transformation relationship between radar measurements and state variables, radar target tracking is described as a state estimation problem for a linear measurement model. This model is useful in practice because it guarantees the whiteness of the measurement noise sequence and the unbiasedness of the target state estimator. Based on this observation, the design of a tracking filter is described as a stochastic minimization problem of an indefinite quadratic form linked to a linear time-varying system whose measurement matrix includes the random coordinate transform uncertainty. The resulting solution almost surely converges to the true state, even when an uncertain coordinate transform matrix is used. Computer simulations show that the proposed filter performs better and more consistently than the existing methods regarding target tracking performance.