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Interval-valued linguistic variables are efficient tools to express the decision makers' uncertain qualitative judgments. Considering the application of interval-valued linguistic variables, this paper proposes an interval distance measure, which is then used to define interval-valued linguistic interval distance measures by combining the 2-tuple linguistic representation model. To reflect the interactions between elements in a set, three correlative interval distance measures on intervalvalued linguistic variables are proposed. Meanwhile, several models designed to obtain the optimal weighting vector are constructed. After that, an approach to pattern recognition and to multi-attribute decision making with interval-valued linguistic information is developed. Meanwhile, associated examples are offered to demonstrate the concrete application of the proposed procedure.

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The security of global navigation satellite system (GNSS) has attracted a lot of attention recently. The spoofing detection method using multi-antenna array is one of the most efficient spoofing detection methods due to its unique geometry space. However, it is either based on the assumption that all spoofing signals come from the same direction or it requires additional inertial measurement unit (IMU) or multi-antenna attitude solution to obtain attitude information. In this paper, we propose a new GNSS spoofing detection method using only two off-the-shelf antennas. This method can detect a single spoofing signal or spoofing signals from multiple directions, and does not require any attitude information. This method employs the carrier phase and the known baseline length to estimate the baseline vector. Its theoretical performance can be assessed by the sum of squared error (SSE) test statistic. Static and dynamic experiments both prove that this method can distinguish the spoofing signal from the real signal effectively without any delay.

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