A novel temporal convolution network-gated recurrent unit (NTCN-GRU) algorithm is proposed for the greatest of constant false alarm rate (GO-CFAR) frequency hopping (FH) prediction, integrating GRU and Bayesian optimization (BO). GRU efficiently captures the semantic associations among long FH sequences, and mitigates the phenomenon of gradient vanishing or explosion. BO improves extracting data features by optimizing hyperparameters besides. Simulations demonstrate that the proposed algorithm effectively reduces the loss in the training process, greatly improves the FH prediction effect, and outperforms the existing FH sequence prediction model. The model runtime is also reduced by three-quarters compared with others FH sequence prediction models.

This paper develops a complete architecture for constant false alarm rate (CFAR) detection based on a goodness-of-fit (GOF) test. This architecture begins with a logarithmic amplifier, which transforms the background distribution, whether Weibull or lognormal into a location-scale (LS) one, some relevant properties of which are exploited to ensure CFAR. A GOF test is adopted at last to decide whether the samples under test belong to the background or are abnormal given the background and so should be declared to be a target of interest. The performance of this new CFAR scheme is investigated both in homogeneous and multiple interfering targets environment.

The rapid hybrid acquisition of PN sequences is proposed for DS/CDMA systems. The system introduces the excision CFAR method into the background power estimation. A mathematical analysis is done for the single path and multipath environments. The detection performance of the proposed scheme is compared with that of other acquisition schemes. Results show that the proposed method has better detection performance if the excision coefficient is properly selected.