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Tactical data links are becoming more widely used in the military, as they can greatly improve the efficiency of various combat units. Such tactical data links must be able to transmit large volumes of data at high speed in complex communication environments while ensuring reliability. This paper studies the influence of 16-ary Quadrature Amplitude...
Contexts in source publication
Context 1
... the structure above, a block diagram of MSK signal quadrature modulation can be obtained, as shown in Figure 8. The MSK signal can be obtained by sequentially performing differential encoding, serial-parallel conversion, two-level modulation, and differentiation of the input information sequence í µí± í µí± . ...
Context 2
... comparing and analyzing, it can be known that this data chain model system can transmit data information reliably. Figure 18 shows that when based on the PN sequence, the two modulation methods' error conditions are pretty different, and the system based on the 16-QAM modulation method has poor error performance. For example, when the signal to noise ratio is 20dB, the error code rate is 0.13. ...
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Citations
... Marine target monitoring plays an important role in the field of marine safety control [1]. At present, the construction scale of information network for marine target detection is increasing, and the construction and deployment of observation stations are showing an obvious rasterization trend [2]. In addition, maritime satellites are widely used to receive AIS signals from marine vessels, and the volume of marine target monitoring data is rapidly expanding [3]. ...
... During the re execution of the algorithm, the time of the data field is discretized to keep the target time of different update frequencies consistent. Thus, the input matrix has a format as follows ( Table 2): (2) lon (2) lat (2) sog (2) cog (2) ... (1) lon (1) lat (1) sog (1) cog (1) ID B t (2) lon (2) lat (2) sog (2) cog (2) ... ...
... During the re execution of the algorithm, the time of the data field is discretized to keep the target time of different update frequencies consistent. Thus, the input matrix has a format as follows ( Table 2): (2) lon (2) lat (2) sog (2) cog (2) ... (1) lon (1) lat (1) sog (1) cog (1) ID B t (2) lon (2) lat (2) sog (2) cog (2) ... ...
Marine target situational awareness is developing towards intelligence. The practical application of marine law enforcement, route control, fishery supervision and other fields makes marine ship track mining and anomaly analysis a hot direction of technology development. At present, most of the research focuses on the feature recognition of single target or single target signal, while the research on correlation anomaly detection based on multi target or multi target signal is relatively less. In this paper, based on the large spatio-temporal data of ocean targets such as radar and AIS track, the corresponding recognition algorithm is designed for the abnormal transport behavior. The real-time batch detection of illegal transshipment is realized, which verifies the effectiveness of the algorithm and shows good performance in practical application scenarios.
This article explores the technology of recognizing non-cooperative communication behavior, with a specific emphasis on analyzing communication station signals. Conventional techniques for analyzing signal data frames to determine their identity, while precise, do not have the ability to operate in real-time. In order to tackle this issue, we developed a pragmatic architecture for recognizing communication behavior and a system based on polling. The method utilizes a one-dimensional convolutional neural network (CNN) to segment data, hence improving its ability to recognize various communication activities. The study assesses the reliability of CNN in several real-world scenarios, examining its accuracy in the presence of noise interference, varying lengths of interception signals, interferences at different frequency points, and dynamic changes in outpost locations. The experimental results confirm the efficacy and dependability of the convolutional neural network in recognizing communication behavior in various contexts.
