Electro-optical/Infrared (EO/IR) camera systems are commonly used in target detection and tracking applications. Such camera systems typically comprise a suite of sensors such as narrow/wide Field of View (FOV) cameras that provide target-originated angular measurements. To estimate the target position in Cartesian space, existing techniques in literature employ the non-linear measurement mapping from the Focal Plane Array (FPA) to azimuth and elevation space. A common assumption made in using this conversion is that azimuth and elevation measurement errors have the same standard deviation, are uncorrelated and are uniform across the camera’s FOV. This paper presents an approach to derive the azimuth and elevation statistics including the cross-correlation of their errors. This approach converts the raw target measurements and their covariance in the image space (FPA) to the angular space for subsequent use in Cartesian state filtering. This conversion has been validated to be unbiased and consistent, and results show that the Line of Sight (LOS) angle error variances and their correlations are in fact variable, with magnitudes dependent on the target’s location in the FPA. The correct LOS angle covariance matrices should be used in Cartesian state estimation and fusion rather than the assumed constant angle variances and uncorrelated errors between the azimuth and elevation.

In this paper, we develop an approach for measurement-to-track association (M2TA) in the presence of (unknown) camera panning and zooming from drone-captured video. Standard M2TA methods assume that the target motion can be used to predict the “measurement association regions” for the bounding boxes. However, if there is a sudden state change due to camera shift (panning) and zooming, it will lead to incorrect associations and poor tracking results. To solve this, the zoom ratio and panning in 2D coordinates are used to describe the camera motion parameters in each frame. The estimated parameters are obtained by a grid search combined with global assignment or directly solved using the linear least squares method, which is also combined iteratively with assignment. The goal is to achieve correct M2TA by adjusting the predicted measurements using the estimated camera parameters. These “improved” predictions can also be used to update the target state with filtering algorithms. Frames with panning or/and zooming from real data are used to illustrate the effectiveness of the proposed methods and compared with the validation gate method based on inflated covariances.

The problem addressed in this paper is the tracking a maneuvering target in a distributed sensor network using a real-world-motivated fusion configuration. The local node SL receives a track from the remote node SR generated by its Interacting Multiple Model (IMM) estimator, and the “inside information” such as motion models, mode probabilities and mode-conditioned estimates of the remote track is unknown. The local node SL has its own measurements which need to be fused with the remote IMM track. This problem can be solved using an existing technique with the following steps: 1) generate a SL track based on its own measurements; 2) perform Track-to-Track Fusion (T2TF) on SR and SL tracks. This paper will develop an alternative approach to fuse the remote IMM track and the local measurements directly. We called it the IMM Cumulated information Fusion (IMM-CFusion). The IMM-CFusion estimates the cumulated information of the local SLmeasurements with multiple models, and then fuses this cumulated information with the remote IMM track state. The IMM-CFusion shows better performance than the T2TF approach in a test case.

This paper considers the problem of target tracking using an asynchronous multistatic system with unknown transmitter positions. In such a system, the receiver is considered as the own sensor to perform passive tracking. It listens to the signals from at least two non-cooperative transmitters via direct and indirect (bouncing from targets) paths. The transmitters and targets are then tracked based on the measured bearings and the bistatic ranges (derived from the TDOA of the direct and indirect path signals). Since the transmitter positions are unknown, they have to be estimated, and their estimates will contain errors. To cope with these errors, we develop an iterated least squares estimator with covariance inflation (ILS-CI) for track initiation, and apply the covariance inflation filter (CIF) for track update. Four approaches, namely the optimal, simple, covariance inflation (CI) and combined approaches, with different strategies in track initiation and track update, are proposed to solve this tracking problem. Their performances are evaluated through simulation tests.

Acoustic propagation delay has not been investigated for a continuous wave multistatic sonar tracking system except for the recent study conducted by Jauffret et al. [4], which estimates the trajectory of a constant velocity target. The results showed that the estimate bias caused by the propagation delay is not negligible, especially for a bistatic system. This paper develops an interacting multiple model unscented Gauss-Helmert filter with numerical Jacobian (IMM-UGHF-NJ) to track a maneuvering target with propagation delay using a bistatic sonar system. The IMM-UGHF-NJ can overcome the two tracking challenges introduced by the delay, namely, implicit state transition model and lack of analytical expression of the Doppler shifted frequency in the measurement model. Simulation tests have been conducted, and the results show that the IMM-UGHF-NJ can reduce the estimation error significantly, especially for fast moving targets.

Intent inference involves analyzing the actions and activities of a target of interest to deduce its purpose. In an environment cluttered with many targets, loaded with information, and under stress, the human may not be able to perform well. Hence a cognitive aid that could derive possible intent inference and monitor the target may help augment human cognition and assist critical human decision making. This paper reports research on two applications: determining the likelihood of weapon delivery by an attack aircraft under military surveillance and conformance monitoring in air traffic control systems. The proposed solution is based on flight profile analysis. Simulation process comprises Interacting Multiple Model-based state estimation and Mamdani-type fuzzy inference to deduce possibilities of weapon delivery and of nonconforming aircraft behavior respectively. Results verify that the method is feasible and provides timely inference that will aid human cognition and hence assist decision making.

This paper will specifically undertake the task of improving the passive sonar system using Self- Organizing Map. Localizing multiple targets is a challenging problem as passive sonar sensors are only able to detect the targets’ bearing angle. An effective way to find the targets location is by triangulation. However, in multi-sensor multi-target environment, ghost targets are introduced during the triangulation process. Self-Organizing Map based on neural network is one of the most recently used methods proposed to extract the true targets. This paper will introduce two improvements to the Self-Organizing Map. The first improvement is to initialize the neurons based on the preliminary triangulation point’s distribution. This results in a faster first-time-seen of the targets. The second improvement is to apply the assumption that each bearing line is associated with only one target. This results in the reduction of the amount of false tracks detected.