Signal Fusion-Based Algorithms to Discriminate Between Radar Targets and Deception Jamming in Distributed Multiple-Radar Architectures

The potential abilities of distributed multiple-radar architectures in the electronic counter–countermeasure are analyzed based on the difference between target echoes and deception jamming in spatial scattering properties. The deception signals received by different receivers are fully correlated, while the correlation of target echoes varies gradually with the interval of view angles. According to this difference, the thought of signal fusion is first adopted and a corresponding algorithm is proposed to discriminate the deception jamming from radar targets in the Neyman–Pearson sense. In this algorithm, active false targets are identified by correlation tests between arbitrary two targets’ complex envelopes in different receivers. To evaluate its discrimination performance, the theoretical expression for the rejection probability of false targets is derived. Simulations verify the feasibility of the new algorithm and its performance improvement over the existing data fusion-based methods. The merit of the method lies in that it can discriminate deception signals generated by arbitrary modulation and can be used in series with data fusion-based methods to improve discrimination performance further.