Integration of both global positioning system (GPS) and inertial navigation system (INS) assures a continuous and accurate navigation system. In low-cost low-precision micro-electromechanical system (MEMS)-based INS/GPS integration navigation systems, one of the major concerns is high-level stochastic noise and uncertainties existing in INS sensors and complex model of real noisy data. In such uncertainty-oriented environments, an intelligence structure with extra degrees of freedom which can handle and model a high-level of uncertainties in INS sensors, and an efficient denoising technique as a precursor to the intelligence structure can be efficient solutions. Our approach to these problems is taken in different steps. First, a denoising technique based on empirical mode decomposition (EMD) is used to provide more accurate INS sensor outputs and better generalization ability. Second, an optimized interval type-2 fuzzy neural network is used to model and handle a high-level of uncertainties efficiently and estimate the positioning error of INS sensors when GPS signals are blocked, and still meet both accuracy maximization and complexity minimization. Fast learning and convergence of the algorithm and less computational complexity can be achieved by using an extended Kalman filter in the learning of algorithm and an accurate and simple type-reduction, respectively, which can be utilized in real-time applications with significant performance. The results of EMD-based denoising technique, as a preprocessing phase, verify superior performance in comparison with the discrete wavelet transform denoising method in the signal-to-noise ratio improvement for raw and noisy signals of INS sensors. To verify the effectiveness of our proposed model, we applied challenging conditions consisting of low-cost low-precision inertial sensors based on MEMS technology, long-term outages of GPS satellites, a high-speed experimental test vehicle and noisy real-world data in the real-time flight experiments. The achieved experimental accuracies are compared with the results that we have achieved in other methods, and our proposed method verifies significant improvements.

中文翻译：

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一种低成本，基于MEMS的集成式INS / GPS车辆导航系统，基于封闭环境中的优化IT2FNN，具有挑战性条件

全球定位系统（GPS）和惯性导航系统（INS）的集成确保了连续准确的导航系统。在基于低成本，低精度的微机电系统（INS）/ GPS集成导航系统中，主要关注的问题之一是高水平随机噪声和INS传感器中存在的不确定性以及真实噪声数据的复杂模型。在这种面向不确定性的环境中，可以处理和建模INS传感器中的高度不确定性的具有额外自由度的智能结构，以及作为智能结构的先驱的高效降噪技术都是有效的解决方案。我们针对这些问题的方法采用了不同的步骤。第一，基于经验模式分解（EMD）的降噪技术用于提供更准确的INS传感器输出和更好的泛化能力。其次，使用优化的区间2型模糊神经网络对GPS信号进行阻塞时，可以有效地建模和处理高水平的不确定性，并估计INS传感器的定位误差，同时仍然满足精度最大化和复杂度最小化的要求。通过在算法的学习中使用扩展的卡尔曼滤波器，可以实现算法的快速学习和收敛，并减少计算复杂度，分别可以在具有显着性能的实时应用中使用精确和简单的类型缩减。基于EMD的去噪技术的结果，作为预处理阶段，与离散小波变换降噪方法相比，在改善INS传感器原始和噪声信号的信噪比方面，证明了其优越的性能。为了验证我们提出的模型的有效性，我们采用了具有挑战性的条件，包括基于MEMS技术的低成本，低精度惯性传感器，GPS卫星的长期停机，高速实验测试车以及嘈杂的真实世界数据。实时飞行实验。将获得的实验精度与我们在其他方法中获得的结果进行比较，并且我们提出的方法验证了重大改进。我们采用了挑战性条件，包括基于MEMS技术的低成本，低精度惯性传感器，GPS卫星的长期停机，高速实验测试车以及实时飞行实验中嘈杂的现实世界数据。将获得的实验精度与我们在其他方法中获得的结果进行比较，并且我们提出的方法验证了重大改进。我们采用了挑战性条件，包括基于MEMS技术的低成本，低精度惯性传感器，GPS卫星的长期停机，高速实验测试车以及实时飞行实验中嘈杂的现实世界数据。将获得的实验精度与我们在其他方法中获得的结果进行比较，并且我们提出的方法验证了重大改进。