Power inversion (PI) arrays have been employed to suppress interference in satellite navigation systems. When the incident direction of a satellite signal is near that of the interference, there will be a significant degradation in the signal to noise ratio (SNR). To reduce this degradation, a PI antenna array composed of a right-hand circular polarized (RHCP) antenna and a spatially-spread electromagnetic vector sensor (SS-EMVS) subarray is proposed, which can suppress interference and acquire satellite signals in both the polarized and spatial domains. Even when the satellite signal is incident from the same direction as the interference, the SNR degradation can be reduced by exploiting the difference in polarization states. It is shown that the filtering is improved in the polarization domain compared with an RHCP PI array, and the SNR loss is lower than with an SS-EMVS PI array while the computational complexity is not increased. Furthermore, the proposed PI array is effective for all Beidou satellite system public service signals.

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Automatic modulation recognition (AMR) is one of the essential parts in the intelligent communication system. In the underwater acoustic communication, it is a challenging work that promptly and easily recognizes the signal modulation schemes by conventional methods. The deep neural network method is a good solution to the problem, which creates a better recognition effect. The packets of data that are fed to the familiar neural network is constant. However, the packets of signal data on the communication course consistently change, which seriously reflects on the signal recognition veracity. A novel deep learning network with the sequence convolutional network in this paper is proposed, which is composed of one-dimensional sequence convolution of residual network modules and the variable convolution kernel range. By extracting the time-domain signal characteristics, the affection of various signal packets can be mitigated. In experiments, the employed network not only has more concentrated on the modulation recognition veracity, but also owns a lower parameter quantity and a shorter training time, which indicates ideal recognition results in the underwater communication environment. Moreover, it is more valuable to the real underwater communication system.