Won-Sang Ra

Time-to-go estimator for missiles guided by BPNG

Biased proportional navigation guidance (BPNG) law is widely used for missiles to home on the target with the required incidence angle. Nowadays the BPNG law has received great interest for sea skimming anti-ship missile (ASM) littoral operation applications. In this paper, we propose a new time-to-go estimator for sea skimming ASMs guided by the horizontal BPNG law. A BPNG problem is formulated in a simple linearized homing geometry and the solution trajectory is introduced. The length of the BPNG trajectory is approximated based on the solution. The time-to-go estimator is derived by applying the Kalman filter theory to the approximated length. Simulation results show that the proposed estimator can estimate the time-to-go very effectively with small computational load.

Aerodynamic Derivatives Identification Using a Non-Conservative Robust Kalman Filter

A non-conservative robust Kalman filter (NCRKF) is applied to flight data to identify the aerodynamic derivatives of an unmanned autonomous vehicle (UAV). The NCRKF is formulated using UAV lateral motion data and then compared with results from the conventional Kalman filter (KF) and the recursive least square (RLS) method. A superior performance for the NCRKF is demonstrated by simulation and real flight data. The NCRKF is especially effective in large uncertainties in vehicle modeling and in measuring flight data. Thus, it is expected to be useful in missile and aircraft parameter identification.

Fast time-frequency domain reflectometry based on the AR coefficient estimation of a chirp signal

In this paper, a novel reflectometry, which is characterized by a simple autoregressive(AR) modeling of a chirp signal and an weighted robust least squares(WRLS) AR coefficient estimator, is proposed. In spite of its superior fault detection performance over the conventional reflectometries, the recently developed time-frequency domain reflectometry(TFDR) might not be suitable for real-time implementation because it requires heavy computational burden. In order to solve this critical limitation, in our method, the time-frequency analysis is performed based on the estimated time-varying AR coefficient of a chirp signal. To do this, a new chirp signal model which contains a sigle time-varying AR coefficient is suggested. In addition, to ensure the noise insensitivity, the WRLS estimator is used to estimate the time-varying AR coefficient. As a result, the proposed reflectometry method can drastically reduce the computational complexity and provide the satisfactory fault detection performance even in noisy environments. To evaluate the fault detection performance of the proposed method, simulations and experiments are carried out. The results demonstrate that the proposed algorithm could be an excellent choice for the real-time reflectometry.

Performance Analysis of the Robust Least Squares Target Localization Scheme using RDOA Measurements

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omni- directional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

Practical direction of arrival estimator using constrained robust Kalman filtering

This paper proposes a linear estimation theory based direction of arrival (DOA) estimator to guarantee high-performance and computational efficiency. To do this, state-space system is derived from the linear prediction relation of the sinusoidal acoustic signal. Since it contains uncertain measurement matrix, the recently developed non-conservative robust Kalman filter (NCRKF) can be applied to compensate the performance degradation by the uncertain measurement matrix. However, unfortunately, the statistical information used in NCRKF scheme may not be precise in actual situation and it leads to the performance degradation. Therefore, in this paper, constrained NCRKF (CNCRKF) is presented to develop practical DOA estimator. It adopts constraint condition derived from the relation between target states to solve the performance degradation problem by the incorrect statistical information. The performance of the proposed solution is demonstrated by the computer simulation.

Compensated robust least-squares estimator for target localisation in sensor network using time difference of arrival measurements

A target localisation estimator based on time difference of arrival (TDOA) measurement is proposed. The localisation estimator is designed in the framework of the recently developed robust least-squares (RoLS) estimator, which provides an unbiased estimation result and can be implemented with a recursive filtering structure. However, when the RoLS estimator is applied to the localisation problem, its localisation performance depends on the knowledge of the stochastic information of the TDOA measurement. This dependency means that incorrectly given information causes localisation error. Therefore to complement the dependency of the given stochastic information of the TDOA measurement, we design a compensation procedure based on the constraints on the state variables of the estimator. The performance of the proposition under several cases of incorrectly given stochastic information is verified through computer simulation, and its filtering structure is compared with other existing localisation algorithms mathematically. In addition, the entire process of the proposed localisation estimator is derived as a recursive form for real-time applications.

A robust horizontal LOS rate estimator for 2-axes gimballed seeker

In this paper, a robust horizontal LOS (line-of-sight) rate estimator for sea skimming anti-ship missile is proposed. A exact LOS rate dynamics model for two-axes gimballed seeker is derived in homing geometry. A new LOS rate estimator is designed by applying a robust Kalman filter to the LOS rate dynamics model. The proposed filter takes into account roll motions and detection gain uncertainties. Simulation results show that the proposed filter performs very well and guarantees the robustness against parameter uncertainties.

Time-to-go estimation filter for anti-ship missile application

In this paper, we propose a new time-to-go estimator for conventional PN guided sea-skimming missiles is proposed. In contrast to the conventional method, our method does not only consider the length of the curved homing trajectory caused by the initial heading error, but also tried to mitigate the influence of the seeker measurement noises. In addition, by adopting a new coordinate system moving together with the target in the estimator design, the effect of target movement on the homing trajectory is efficiently took into account. Simulation results show that the proposed filter can estimate the time-to-go very accurately with small computational load.

Linear recursive target state and time-to-collision estimator for automotive collision warning system

This paper proposes a new linear recursive target state and time-to-collision estimator for the development of the automotive collision warning system. The addressed problem can be cast into the representative nonlinear state estimation under cluttered environment. To prevent the tracking performance degradation due to the inherent nonlinearity between the polar coordinates measurements of the automotive radar and the target state, a practical linear filter design scheme employing the estimated line-of-sight (LOS) Cartesian coordinate system (ELCCS) is proposed. ELCCS is redefined by using a priori LOS estimates in every update period in order to ensure the unbiasedness of the proposed linear tracking filter. Moreover, in order to effectively cope with the cluttered environment and to enhance the target tracking performance, a modified probabilistic data association filter (MPDAF) is newly proposed. Finally, using the most probable closing velocity and range measurements within the validation region, a quasi-optimal linear robust time-to-collision (TTC) estimator is designed. For its linear recursive filter structure, the proposed method is more suitable for the development of the performed and reliable collision warning system. The performance of the proposed scheme is demonstrated by computer simulations.

Optimal guidance cut-off strategy based on closed-form PNG solution for single lag system

A systematic way to determine both the guidance cut-off time and the terminal acceleration command is newly proposed for missiles guided by the proportional navigation (PN) toward a non-maneuvering target. In order to analyze the diverging behavior of the guidance loop in terminal phase, the actual PN guidance loop with first-order dynamic lag is modeled as the confluent hyper-geometric differential equation of relative kinematics and its closed-form solution is first derived. Based on this analytic solution, the optimal guidance cut-off problem is formulated as the minimization of a quadratic cost in terms of miss-distance and the constant acceleration command issuing after guidance cut-off time. By solving this optimization problem, it is shown that there always exists the guidance cut-off time nullifying zero-effort miss. Through the typical homing guidance scenarios for the stationary target, it is demonstrated that the proposed method guarantees the perfect interception even in the presence of the dynamic lag in the PN guidance loop.