Acceleration commands in missiles guided by proportional navigation require the measurement of line-of-sight (LOS) rate. It is often obtained by filtering the output of a two-degree-of-freedom (2-DOF) rate gyro mounted on the inner gimbal of the seeker. This paper describes the modeling of an imaging seeker and the formulation of an extended Kalman filter (EKF) for the estimation of LOS rate from measurements of relative angular displacement between seeker gimbals and a low-cost strapdown inertial unit. The approach aims at circumventing the need for the rate gyro on the seeker. A linearizing feedback control law for decoupling missile motion from that of the seeker is proposed based on the filter model and its estimates. Additionally, the control law uses visual information from the image sequence for target tracking. Seeker dynamics and control are then integrated into a dynamic model of a cruciform missile equipped with canards and rollerons and guided by proportional navigation in three-dimensional (3-D) interception tasks. MonteCarlo simulation is employed to evaluate the overall system accuracy subject to different initial conditions-lateral and head-on engagements-and the impact of rolling motion during highmaneuvers on miss distance. The validation model includes noise in the various sensors, coupled inertia of the seeker gimbals, signal saturation at various subsystems, optical geometric distortion, and target segmentation errors in the image plane. Initial engagement geometry and roll-rate damping at high incidence angles have been observed to have a significant impact on miss distance.

This article investigates the performance of three distinct approaches to nonlinear filtering applied to a simulated three-axis satellite testbed used for evaluating attitude estimation and control algorithms: extended and unscented Kalman filters and a regularized particle filter. Each approach is numerically evaluated with respect to attitude and angular rate estimation accuracy, computational workload, convergence rate under uncertain initial conditions, and sensitivity to disturbances.

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We present a method to acquire 3D position measurements for decentralized target tracking with an asynchronous camera network. Cameras with known poses have fields of view with overlapping projections on the ground and 3D volumes above a reference ground plane. The purpose is to track targets in 3D space without constraining motion to a reference ground plane. Cameras exchange line-of-sight vectors and respective time tags asynchronously. From stereoscopy, we obtain the fused 3D measurement at the local frame capture instant. We use local decentralized Kalman information filtering and particle filtering for target state estimation to test our approach with only local estimation. Monte Carlo simulation includes communication losses due to frame processing delays. We measure performance with the average root mean square error of 3D position estimates projected on the image planes of the cameras. We then compare only local estimation to exchanging additional asynchronous communications using the Batch Asynchronous Filter and the Sequential Asynchronous Particle Filter for further fusion of information pairs' estimates and fused 3D position measurements, respectively. Similar performance occurs in spite of the additional communication load relative to our local estimation approach, which exchanges just line-of-sight vectors.