###### Abstract  
To tackle the problem of difficult signal detection and carrier frequency synchronization faced by wireless communication among stations of the multistatic passive detection system in interference environments, an adaptive signal detection and carrier frequency offset (CFO) estimation method based on a virtual array is proposed in this paper. This is a data-aided method that utilizes a training sequence composed of three segments of sub-training sequences with different symbols. This method first uses spatial spectrum estimation to obtain the coarse frequency estimations of interference signals and CFO from virtual array signals constructed from the first two sub-training sequences. Then, beamforming is conducted accordingly on the virtual array signals constructed from the third sub-training sequence to suppress the in-band interferences and protrude the expected signal. Finally, improved performance of signal detection and CFO estimation is obtained with the beamformed signals. Simulation experiments show that a missed detection probability as low as \(1\times 10^{-4}\), with a false detection probability of \(1\times 10^{-3}\), can be obtained under a signal-to-interference ratio (SIR) of \(-10\) dB and \(E_{b}/N_{0}\) of \(1\) dB. Moreover, the proposed method can also simultaneously achieve a CFO estimation error that is lower than \(3\%\), with the condition of \(E_{b}/N_{0}\) being as low as \(-5\) dB under different SIRs. Simulation results validate the proposed method and demonstrate the promising application prospects of the proposed method in networked passive detection scenarios. n  
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mitigate the influence of in-band interferences on communication quality. However, such technology is bandwidth-demanding. In order to achieve spread spectrum communication, additional bandwidth greater than the information bandwidth is required, which is not conducive to mitigating the frequency congestion and improving the utilization efficiency of spectrum resources. With the rapid advancement of deep learning, the artificial intelligence-based method for in-band interference suppression has also been studied. Recently, a method based on a convolutional neural network and recurrent neural network with a gated recurrent unit for in-band interference suppression has been proposed in [10], which exhibits promising suppression performance in strong interference scenarios and is capable of effectively reducing the bit error rate. However, such a method demands a large amount of training data, which is not always available and will make the method hard to use in situations that require rapid deployment. With the advantages of high estimation accuracy, low computational complexity, and ease of implementation, data-aided methods have become an important research direction for signal detection and CFO estimation in the field of communication. Such methods utilize the a priori information of the training sequence to construct timing metrics for signal detection, as well as to estimate CFO [11; 12; 13; 14; 15]. The most typical feature of such methods is that they generally construct timing metrics through the autocorrelation of the training sequence. In situations of high signal-to-noise ratio (SNR), it is easy to detect the timing of signal arrival through such timing metrics since the expected signal is much stronger than the noise. However, existing methods usually build the signal model without consideration of the impacts of in-band interferences. Thus, their performance in signal detection and CFO estimation cannot be ensured in the situation of networked passive detection in which interferences exist. Through extensive simulation experiments, we find that when there are strong in-band interferences in the baseband signal; the timing metric constructed by autocorrelation can easily fail to distinguish the noise, interferences, and expected signal since the training sequence is disturbed, which could lead to a significant increment of false detection probability and missed detection probability.