Learning-Based Space-Time Adaptive Processing

Abstract

The probability of target detection in airborne-radar missions depends on the target signal-to-interference-plus-noise ratio. In order to maximize the probability of detection, it is necessary to maximize the target signal-to-interference-plus-noise ratio by suppressing the interference to an acceptable level. The type of interference encountered by airborne radars is of a distinctive nature; it spreads in both the spatial and the temporal dimensions, exhibiting a relationship between the amount of Doppler shift in the temporal dimension and the spatial direction of the echo source. In practical situations, the characteristics of the interference present are not known a priori; thus, they have to be estimated in real-time. The two-dimensional nature of the unknown interference dictates the use of two-dimensional adaptive filters to suppress it. Such filters are called space-time adaptive filters. In practical situations, the amount of secondary training data needed to accurately compute the space-time adaptive filter weights is not available. Thus, it is necessary to develop algorithms that are able to suppress the unknown interference with limited amounts of training data. Many such algorithms have been developed over the past few decades, each with its own advantages and drawbacks. In this report, a new algorithm called "learning-based space-time adaptive processing" is proposed. The proposed algorithm transforms the filtering problem into a pattern classification problem, where the secondary data is used to train a classifier, instead of estimating the interference characteristics. The results show that the proposed algorithm achieves a higher target signal-to-interference-plus-noise ratio than space-time adaptive processing when the amount of secondary data is limited and the target power is not extremely low compared to interference power. The proposed system is able to overcome two more problems faced by space-time adaptive processing: target-cancellation and clutter variation. Finally, a cascaded system of space-time adaptive processing followed by learning-based space-time adaptive processing is proposed. The cascaded system offers a performance gain compared to the individual systems.