Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods.

The design of a modern Global Navigation Satellite System (GNSS) has been exceptionally valued by the military and civilians of various countries. The inclusion of the pilot channel in addition to the navigation data channel is considered one of the major changes in GNSS modernization. Schemes of an equal weight combination (1:1 combination) and power ratio combination for data and pilot are primarily adopted by traditional receivers. With the emergence of the new data and pilot modulation signals with unequal power, such as L1C at Global Positioning System (GPS) L1 frequency and B1C at BeiDou Navigation Satellite System (BDS) B1 frequency, the traditional combination coefficient cannot achieve optimal reception performance. Considering the influence of the combination coefficient on the reception performance, the optimal coefficient of the correlator joint is estimated in this paper. The entire architecture of the data/pilot correlator joint tracking and positioning with unequal power is given. Based on the equivalence principle of the correlator joint and the discriminator joint, the optimal coefficient of the carrier loop is determined. A mathematical model of joint code tracking accuracy is established, and the optimal coefficient of the code loop is determined. The real-life satellite signal and simulation results show that the amplitude–ratio combined scheme is the best for receiving of correlator joints, followed by the power–ratio combination scheme and, finally, the 1:1 combination scheme. It is worth mentioning that the positioning accuracy of the amplitude–ratio combination is improved by 2% compared to the 1:1 combination, and by 1.3% compared to the power–ratio combination for B1C signal. The positioning accuracy of the amplitude–ratio combination is improved by 2.37% compared to the 1:1 combination, and by 1.6% compared to the power–ratio combination for L1C signal. The conclusions of this paper are validated for the traditional data/pilot with an equal power allocation. The techniques and test results provide technical support for GNSS high-precision-user receivers.

In the field of radio surveillance and cognitive radio, the reception of a signal is usually made in a non-cooperative manner, which means there exists little prior information to detect the signals reliably via a traditional method. At the same time, the prevalent wideband acquisition mode will receive multiple subband signals from homogeneous or heterogeneous systems, leading to deteriorated detection performance under a non-flat spectrum and fading channel. In view of the above concerns, a novel detection algorithm based on the Gaussian hidden Markov model (HMM) is proposed so as to separate the individual sub-band signal from the wideband spectrum accurately in a low signal-to-noise ratio (SNR). The simulated communication signals with spectral fluctuation and multipath fading indicate the superiority and applicability of the proposed algorithm as compared with other detection algorithms. Our algorithm can achieve a 94% detection probability at −10 dB SNR under an additive white Gaussian noise (AWGN) channel and has a nearly ideal receiver operating characteristic (ROC) curve. When faced with a Rayleigh fading channel, it still outperforms other algorithms. The acquired real data also very its practical application with moderate computation complexity and a more stable carrier-frequency estimation.

As a basic task and key link of space situational awareness, space target recognition has become crucial in threat analysis, communication reconnaissance and electronic countermeasures. Using the fingerprint features carried by the electromagnetic signal to recognize is an effective method. Because traditional radiation source recognition technologies are difficult to obtain satisfactory expert features, automatic feature extraction methods based on deep learning have become popular. Although many deep learning schemes have been proposed, most of them are only used to solve the inter-class separable problem and ignore the intra-class compactness. In addition, the openness of the real space may invalidate the existing closed-set recognition methods. In order to solve the above problems, inspired by the application of prototype learning in image recognition, we propose a novel method for recognizing space radiation sources based on a multi-scale residual prototype learning network (MSRPLNet). The method can be used for both the closed- and open-set recognition of space radiation sources. Furthermore, we also design a joint decision algorithm for an open-set recognition task to identify unknown radiation sources. To verify the effectiveness and reliability of the proposed method, we built a set of satellite signal observation and receiving systems in a real external environment and collected eight Iridium signals. The experimental results show that the accuracy of our proposed method can reach 98.34% and 91.04% for the closed- and open-set recognition of eight Iridium targets, respectively. Compared to similar research works, our method has obvious advantages.

The widespread 5G base stations can be potential jammers for the vulnerable BeiDou B1I receivers due to its low power. Therefore, a novel analytical model is derived for the 5G signal to evaluate its impact on acquisition performance under three decision methods. The good agreement between the Monte Carlo method (MCM) through software defined receiver (SDR) and the derived expressions validates the effectiveness of the proposed algorithm. It can be found that the receivers exhibit varied responses for different 5G waveforms and decision strategies. The receiver also shows the least endurances for some kind of 5G waveforms, however, this kind of adverse effect can be cancelled by a reduced interference signal ratio (ISR), an increased integration time or a larger accumulation times.