Over the span of more than 2 decades, AUG Signals has developed several technologies in the areas of target tracking, data association and data fusion. These capabilities are applied to a wide range of sensors (e.g., radar, sonar, ESM, IR, SAR etc.) to process sensor information individually or collectively in order to achieve robust surveillance capability in both military and civilian applications.
AUG Signals’ target tracking capabilities consist of the following technologies below.
AUG Signals has developed superior tracking algorithm that improves the tracking performance metric (e.g., track accuracy, track completeness, track consistency) by up to 40% compared to the state of the art.
For robust performance in the presence of high clutter, (or high track density) tracks are divided into two categories; confirmed tracks and initial tracks. Confirmed tracks are validated while initial tracks are yet to be validated. AUG Signals applies a robust decision mechanism for track initialization, confirmation and deletion, which is based on track length, number of misdetections and the degree of association between the track and measurements. In addition, a three-layer association approach is used to associate a set of measurements with existing tracks and generate new tracks. This approach gives higher precedence to the targets in which the tracker has higher confidence.
AUG Signals uses multidimensional data association between a set of tracks and multiple sets of measurements to significantly improve data association performance and, consequently, tracking performance. The multidimensional association is sequentially performed by m-best 2-D association algorithm. This algorithm borrows from Multiple Hypothesis Tracking (MHT) concept which refines various hypotheses as more data is available. Our association algorithm significantly reduces the computational load of MHT while maintaining similar level of performance, and is used for both active and passive measurements. Our track initiation unit generates new tracks using measurements that are not associated with currently tracked targets. While measurements from single passive sensor are used to generate angle only tracks, those from multiple passive sensors are used to obtain tracks in the Cartesian coordinate system. The state of the art of multiple passive sensor based tracking uses multidimensional association algorithm, which requires the full association tree. AUG Signals’ passive sensor based tracker associates measurements directly with tracks, which drastically reduces the association tree size and hence, the computational complexity (up to a factor of 10). In addition, there is up to 20% improvement in data association performance.
AUG Signals has significant experience in designing state of the art tracking filters for various applications. Interacting Multiple Model (IMM) filters based on Kalman models, with modes tuned to both linear motions and maneuvers, have been designed for tracking highly maneuvering targets. AUG Signals has developed an efficient algorithm to choose the IMM modes given the target types present in any scenario. For scenarios with nonlinear state transition and/or nonlinear measurement (e.g., ESM sensors) the Kalman filter is replaced by extended Kalman filter. In systems with high degree of nonlinearity Unscented Kalman Filter (UKF) is used. In addition, AUG Signals has significant experience in particle filter based tracker design.