KEYWORDS: Signal detection, Analog to digital converters, Digital signal processing, Signal processing, Mathematical optimization, Quantization, Transceivers, Signals intelligence, Control systems
In order to adapt to the special requirements of different measurement systems for channel simulators, especially the differentiated needs of input and output signal power, this paper proposes an innovative design method for intelligent channel simulators based on adaptive link optimisation of signal power. The design adopts an all-link power adaptive link optimisation algorithm, which realises the precise control and intelligent adjustment of the signal power of each link inside the channel simulator. Compared with the traditional methods of input link power independent control and output link power independent control, this method maintains the best output signal quality through the adaptive detection and matching of RF input and output signal power, as well as the fast response power adjustment of key components such as the receiving channel, the sampling preprocessing module, and the signal recovery processing module, and so on. The test results show that the intelligent channel simulator can automatically identify and match the input and output signal powers of different systems under test, the output signal power accuracy converges rapidly, the signal quality is good in a large dynamic range of input and output power, and it meets the design requirements of channel simulators in the field of modern wireless communication in terms of intelligence and accuracy control.
Improving the reliability of integrated navigation systems requires effective fault detection and isolation strategies. In response to false alarms in jump soft fault detection grounded on the orthogonality principle, this study proposes refinements to the approach for data selection within a sliding window. The refined method introduces an improved orthogonality fault detection algorithm, which enhances detection accuracy and reduces the likelihood of false alarms. By harnessing data within the sliding window optimally, the algorithm computes the orthogonal average as the test statistic without necessitating an increase in the sliding window length. Furthermore, by combining the traditional residual chi-square test and extrapolation chi-square test, more precise judgments can be made regarding fault type and occurrence time. Ultimately, through the integration of traditional residual chi-square and residual extrapolation chi-square detection, this methodology enhances fault classification capabilities, reduces false alarm rates, and enables the effective identification of pulse, abrupt, and gradual faults even in scenarios with limited fault signal strength.
Beidou dual-mode receiver has the functions of positioning, navigation, timing and message communication, and has been widely used in various industries. Based on the transition from the BDS-2 regional system to the BDS-3 global system, the impact of the upgrade of BDS on the service performance of the Beidou dual-mode receiver and the countermeasures are studied. Firstly, the differences between BDS-2 and BDS-3 in signal type, constellation scale and service performance are compared. Next the system composition and working principle of Beidou dual-mode receiver are briefly introduced. Then combined with the specific application scenario, the impact of the upgrade of BDS on the service performance of the Beidou dual-mode receiver are analyzed. Finally, the countermeasures of Beidou dual-mode receiver are discussed to reduce or eliminate the impact of BDS upgrade on the service performance of the receiver, and provide continuous and reliable services for users. The research results can provide reference for the design, development and application of Beidou dual-mode receivers.
KEYWORDS: Modeling, Data modeling, Internet of things, Reliability, Machine learning, Resistance, Control systems, Mathematical modeling, Process modeling, Network security
With the rise of the Internet of Things (IoT), some emerging mobile devices have been widely used such as wireless sensor networks, Radio Frequency Identification (RFID) chips, and smart cards etc. However, their communication security issues in open environments are increasingly prominent. Physical Unclonable Function (PUF) is a new type of "hardware fingerprint" that can authenticate IoT devices in the aspect of hardware. However, the Challenge-Response Pair (CRP) mechanism of PUF is vulnerable to Machine Learning (ML) modeling attacks. Based on this, the paper proposes a Dynamic Adversarial (DA) PUF through modifying the original CRP mechanism of APUF. Experimental results show that the PUF can effectively resist ML modeling attacks, while maintaining good uniformity, uniqueness, and reliability.
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