Waveform diversity technology is an effective anti-jamming measure against range false target jamming. However, in an environment of strong energy jamming, the high side-lobe caused by the jamming signal mismatch will still affect the detection performance of the radar. To this end, a dictionary learning method is proposed in order to better suppress and eliminate high-power jamming.

First, an initialization dictionary corresponding to the target and jamming signals is established. Second, the initialization dictionary and selected atoms are used to generate an autocorrelation matrix template, and the matching coefficient is obtained using the non-homogeneous linear mean square estimation. Next, an approximate quasi-Karhunen-Loève transform（Q-KLT） basis corresponding to the target and jamming signals is constructed by template and matching coefficients respectively. Finally, a convex optimization algorithm is used to separate and recover the target and jamming signals.

The simulation results show that the proposed method can effectively counter the jamming of one or multiple range false targets at a 30 dB jamming-to-signal ratio.

Compared with the traditional waveform diversity technology, the proposed method still maintains good anti-jamming performance in high jamming-to-signal ratio environments, and can be used by shipboard radar to counter range false target jamming.