NATO STO

In the field of radar there is a growing need for higher resolutions to enable the detection of small targets in a complex background, to improve the tracking of multiple closely spaced targets, and to support automatic target recognition. Recently, Compressive Sensing (CS) emerged as a technique that enables to achieve higher resolution than conventional methods using a combination of random sampling and sparse signal reconstruction algorithms. In the past decade, a lot of theoretical progress has been made in CS and simulations have demonstrated the potential of CS methods to improve radar performance. However, the adoption of CS techniques in operational systems is still lagging behind the theoretical and algorithmic advances due to practical constraints such as latency, memory size, and power consumption dictated especially by the iterative nature of the algorithms used in CS. In parallel, in recent years Machine Learning (ML) has achieved tremendous success in many commercial applications such as automatic face recognition, speech recognition, natural language processing, autonomous vehicles, and robotics. An important element in the success of ML for these applications is the availability of large databases which are used in training. Machine Learning has the potential to provide computationally efficient approaches to improve target detection, tracking and classification in radar with enhanced resolution. However, Machine Learning techniques, and in particular deep neural networks, are often poorly understood from a theoretical perspective due to their black box nature. An integration of Compressive Sensing and Machine Learning for radar applications offers the potential to combine the benefits of both worlds. For example, iterations of many CS algorithms for sparse signal recovery have the structure of neural network layers. Therefore, computationally efficient ML models such as Deep Neural Networks can be used to replace the expensive iterations with fixed depth feedforward networks learned from the data. Furthermore, the learning process permits extraction of better dictionaries for sparse signal representation from training data. On the other hand, CS based generative models of target and clutter could possibly be used to produce vast training sets required for ML algorithms filling in the gaps in measured data by means of online predictions from a “compressed” database, improving the generalization process. This CS assisted training strategy will significantly widen the scope of problems where ML could be successfully applied, as in the military domain the availability of a large measured radar data training sets cannot always be guaranteed. In addition, for undersampled and interrupted datasets encountered in multi-function radar systems, CS techniques can be used as preprocessors feeding into ML architectures that cannot effectively impute missing data in these scenarios.