Radar high-resolution range profiles target recognition based on stable dictionary learning

Sparse representation models based on dictionary learning have led to interesting results in signal restoration and target recognition. However, due to the redundancy defined by overcomplete dictionary atoms in new space, finding sparse representations from inaccurate measurements may cause uncertainty and ambiguity. Especially for radar automatic target recognition using high-resolution range profiles (HRRP), the target-aspect sensitivity, amplitude fluctuation and outliers in HRRPs could result in mismatch among the sparse representations of the same class and thus deteriorate the recognition performance. This article proposes a novel stable dictionary learning method to deal with this problem and improve the pattern recognition performance. The proposed method relies on the constraints that the sparse representations of adjacent HRRPs without scatterers' motion through range cells should have the same support and lower variance. The structured sparse regularisation is then used to automatically select the optimal dictionary basis vectors for stable sparse coding. Experiments based on the measured HRRP dataset validate the performance of the proposed method. Moreover, encouraging results are reported with small training data size and under different signal-to-noise ratio conditions.