Seul-Ki Han

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.

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.

Missile radome error compensation using modified interacting multiple model Kalman filter

This paper addresses the compensation problem of the missile radome aberration error using the interacting multiple model (IMM) algorithm. To compensate the radome error, the proposed method estimates the radome slope by applying the modified IMM algorithm. In the proposed approach, the mode probability update in the IMM algorithm is redefined for improving performance of the radome slope estimation by reflecting the recent tendency of the radome slope hypotheses. This is enabled by utilizing the new weighting function including previous information of likelihood function and mode probability during certain period. Besides, by designating boresight error and LOS rate as the state for the system model, the linear estimation technique is used instead of the nonlinear estimation method. Therefore, the proposed method is suitable for the real-time implementation due to the linear filter structure. Through the simulation results on the typical engagement missile-target scenario, the reliability and performance of the proposed radome slope estimator is evaluated.

A novel missile warhead tracking algorithm based on geometric data association

This paper proposes a new ballistic missile warhead tracking method for enhancing the anti-ballistic capability of a FMCW seeker used for ballistic missile defense (BMD) system. Under the assumption that the target has a single dominant scatterer, most FMCW radars adopt the well-known paring algorithm to obtain range and range rate information. However, the ballistic missile is generally composed of many scatterers and the warhead section of concern is not a dominant one. Therefore, the conventional paring algorithm may not be applicable for the ballistic missile warhead tracking. To overcome this limitation, the problem is reformulated as tracking of multiple scatterers of a ballistic missile in view of data association. Furthermore, an inventive hypotheses validation scheme is newly devised by exploiting the geometric constraint among scatterers corresponding to the ballistic missile warhead and its center of body. Therefore, the proposed geometric data association filter can drastically improve the warhead tracking performance in practice. Through computer simulations, the effectiveness and the reliability of the proposed method are demonstrated.

Linear Robust Target Tracking Filter Using the Range Differences Measured By Formation Flying Multiple UAVs

This paper addresses a new passive target tracking problem using the range differences measured by cooperative UAVs. In order to solve the range difference based passive target tracking problem within the framework of linear robust state estimation, the uncertain linear measurement model which contains the stochastic parameter uncertainty is derived by using the noisy range difference measurements. To cope with the performance degradation due to the stochastic parameter uncertainty, the recently developed non-conservative robust Kalman filtering technique [1] is applied. For the cruciform formation flying UAVs, the relationship between the target tracking performance and the measurement errors is quantitatively analyzed. The proposed filter has practical advantages over the classical nonlinear filters because, for its recursive linear structure, it can provide satisfactory convergence properties and is suitable for real-time multiple UAVs applications. Through the simulations, the usefulness of the proposed method is demonstrated.