Taek Lyul Song

Observability of target tracking with bearings-only measurements

Observability requirements are obtained for three-dimensional maneuvering target tracking with bearings-only measurements. The approach taken here using pseudomeasurements is completely different from the previous work in this area in that it does not involve solutions of nonlinear differential equations resulting from complicated time differentiations and it can treat a more general class of target motions not restricted to N th-order target dynamics.

Impact angle control for planar engagements

A new and practical terminal guidance law is presented for impact angle control for two-dimensional active homing engagement scenarios. Impact angle control is required to enhance terminal effectiveness of antitank and antiship missile systems in particular. The proposed guidance law is developed to cope with missile velocity reduction due to aerodynamic drag and target maneuver. In actual applications, the proposed guidance law is combined in cascade with a target tracking filter under the certainty equivalence principle. The proposed guidance law in conjunction with the target tracking filter is shown to be effective in antiship active homing engagement scenarios by a series of Monte Carlo simulation runs.

Observability of target tracking with range-only measurements

A necessary and sufficient condition for local system observability, a prerequisite to target motion analysis, is presented in this technical communication for two-dimensional manoeuvring target tracking with range-only measurements from a single observer. The approach taken in this paper utilizes the Fisher information matrix developed from the analytical treatment of system dynamics and noisy measurement equations established in a modified polar coordinate system. The analytical results of this paper are demonstrated by a series of simulation studies for applications on naval surface vehicle engagements.

Practical guidance for homing missiles with bearings-only measurements

Observability is analyzed for the target tracking with bearings-only measurements (BOM) applied to a passive homing missile system using augmented proportional navigation guidance (APNG). Based on the analysis, a practical homing guidance law is proposed to enhance observability for tactical missile applications. The new guidance law is tailored to provide oscillation of line-of-sight (LOS) angle induced by initial missile heading errors without sacrificing terminal effectiveness. Simulation studies indicate that the proposed guidance law provides convergence of the state estimates essential to homing guidance applications as well as terminal effectiveness.

Time-optimal impact angle control for vertical plane engagements

As a part of trajectory modulation to increase terminal effectiveness, impact angle control is required in the terminal phase of tactical missile systems. Moreover, the missile systems are allowed to have only low altitude and minimum flight time to reduce the probability of detection by sensors of missile defence systems and thus to increase survivability. In this paper, a simple numerical solution for a time-optimal control law is suggested in the case of constrained missile maneuverability and impact angle for a first-order-lag autopilot. The numerical solution utilizes an analytic solution of a simplified problem with a zero-lag autopilot assumption to determine optimal switching instants. Performance of the proposed control law is tested by a simulation study.

Adaptive Clutter Measurement Density Estimation for Improved Target Tracking

In a surveillance situation the origin of each measurement is uncertain. Each measurement may be a false (clutter) measurement, or it may be a target detection. Probabilistic methods are usually used to discriminate between the clutter and the target measurements. Clutter measurement density is an important parameter in this process. The values of the clutter measurement density in the surveillance space are rarely known a priori, and are usually estimated using sensor data and track information. A novel approach is presented and evaluated for estimating the values of clutter measurement density, which significantly enhances target tracking. Simulation results validate this approach.

A probabilistic nearest neighbor filter algorithm for tracking in a clutter environment

A new probabilistic nearest neighbor (NN) filter algorithm which accounts for the probability that the NN measurement is a false one is proposed to improve the performance of the NN filter. The NN filter is the most popular and widely used algorithm for target tracking in clutter due to its computational simplicity. The proposed algorithm is derived from establishing probability density functions conditioned on all the possible events related to the NN measurement. The resulting algorithm is different from the existing probabilistic nearest neighbor filter (PNNF) algorithm. The performance of the proposed algorithm is analyzed and compared with that of the NNF. The proposed algorithm for aerial target tracking in a clutter environment is tested by a series of Monte Carlo simulation runs. Simulation results are also compared with the off-line performance prediction algorithm developed in this paper.

A probabilistic nearest neighbor filter algorithm for m validated measurements

The probabilistic nature of the nearest neighbor measurement in a cluttered environment is shown to be varying with respect to the number of validated measurements. Incorporating the number of validated measurements into the design of the probabilistic nearest neighbor filter (PNNF) produces a new data association proposed in this correspondence. The proposed algorithm for aerial target tracking in a cluttered environment is tested by a series of Monte Carlo simulation runs, and it turns out that the new filter has less sensitivity for the unknown spatial density of false measurements and better tracking performance than the existing PNNF that does not utilize the current number of validated measurements

Target Tracking With Target State Dependent Detection

Target tracking algorithms usually treat the probability of detection as independent of the target state. In most cases, this assumption is not true, with subsequent degradation in the target tracking performance from both expected and optimal levels. One typical example is the Doppler frequency based clutter rejection, the other is obfuscation (shadowing) of ground based targets, and the third is antijamming notch filtering. This dependence of the probability of target detection on the target trajectory state modulates the measurement likelihood, which, in turn, introduces measurement nonlinearity. In this paper, we first present a general algorithm for target tracking in clutter when the probability of detection is target state dependent, and then proceed to an algorithm where both target state estimate and the probability of detection are modeled by Gaussian mixtures. The probability of target existence is recursively updated as the track quality measure used for false track discrimination. A two-sensor-based ground maneuvering target tracking in clutter simulation validates this approach.

Iterative joint integrated probabilistic data association for multitarget tracking

In situations with a significant number of targets in mutual proximity (close to each other), optimal multitarget data association approach suffers from the numerical explosion. This severely limits the applicability, i.e., the number of close targets that may be reliably tracked.We propose an iterative implementation of the joint integrated probabilistic data association (JIPDA) which allows a performance/computation resources tradeoff. This approach can also be incorporated into joint integrated track splitting (JITS). The iterations start with the single target integrated probabilistic data association (IPDA) and each subsequent iteration improves the approximation towards JIPDA, reaching the optimal multitarget solution within a finite number of iterations.