Intent inference is about analyzing the actions and activities of an adversarial force or target of interest to reach a conclusion (prediction) on its purpose. In this paper, we report one of our research works on intent inference to determine the likelihood of an attack aircraft being tracked by a military surveillance system delivering its weapon. Effective intent inference will greatly enhance the defense capability of a military force in taking preemptive action against potential adversaries. It serves as early warning and assists the commander in his decision making. For an air defense system, the ability to accurately infer the likelihood of a weapon delivery by an attack aircraft is critical. It is also important for an intent inference system to be able to provide timely inference. We propose a solution based on the analysis of flight profiles for offset pop-up delivery. Simulation tests are carried out on flight profiles generated using different combinations of delivery parameters. In each simulation test, the state vectors of the tracked aircraft are updated via the application of the Interacting Multiple Model filter. Relevant variables of the filtered track (flight trajectory) are used as inputs to a Mamdani-type fuzzy inference system. The output produced by the fuzzy inference system is the inferred possibility of the tracked aircraft carrying out a pop-up delivery. We present experimental results to support our claim that the proposed solution is indeed feasible and also provides timely inference that will assist in the decision making cycle.
In this paper, we describe a deghosting algorithm in multiple passive acoustic sensor environment. In a passive acoustic
sensor system, a target is detected by its bearing to the sensor, and the target location is obtained from triangulation of
bearings on different sensors. However, in multi-passive sensor and multi-target scenario, triangulation is difficult. This
is because multi-target triangulation results in a number of ghost targets being generated. In order to remove the
triangulating ghosts, the deghosting technique is essential to distinguish the true targets from the ghost targets. We
suggest a deghosting algorithm by applying Bayes’ theorem and the likelihood function on the acoustic signals. A
probability related to acoustic signal on each triangulating point is recursively computed and updated at every time
stamp or frame. The triangulating point will be classified as a true target, once its probability exceeds a predefined
threshold. Furthermore, acoustic signal has propagation delay. The situation yields the triangulating location biased to
the bearing of the nearest sensor. In our algorithm, the propagation delay problem is solved by matching the histories of
bearing tracks, and yields the unbiased location that has similar emitting times for the sensors contributing to the
triangulation point. The emitting times can be derived from detecting times and propagation delays. Performance result
is presented on simulation data.