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A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments

机译:基于低成本的集成MEMS的INS / GPS车辆导航系统,基于闭塞环境中的优化IT2FNN优化的条件

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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.
机译:全球定位系统(GPS)和惯性导航系统(INS)的集成确保了连续和准确的导航系统。基于低成本低精度微机电系统(MEMS)基于INS / GPS集成导航系统,主要问题之一是存在于INS传感器中的高级随机噪声和不确定性,以及实际嘈杂数据的复杂模型。在这种不确定的环境中,一种智能结构,具有额外的自由度,可以处理和模拟INS传感器中的高级别的不确定性,以及作为智能结构的前体的有效的去噪技术可以是有效的解决方案。我们对这些问题的方法采用不同的步骤。首先,使用基于经验模式分解(EMD)的去噪技术来提供更准确的INS传感器输出和更好的泛化能力。其次,优化的间隔类型-2模糊神经网络用于有效地模拟和处理高级不确定性,并在GPS信号被阻塞时估计INS传感器的定位误差,并且仍然满足精度最大化和复杂性最小化。通过在算法的学习中使用扩展卡尔曼滤波器和精确且简单的类型减少,可以实现算法的快速学习和较少的计算复杂度,可以在具有显着性能的实时应用中使用。基于EMD的去噪技术的结果,作为预处理阶段,验证了与INS传感器原始和嘈杂信号的信噪比改进中的离散小波变换去噪方法相比验证了卓越的性能。为了验证我们所提出的模型的有效性,我们基于MEMS技术,GPS卫星的长期停用,高速实验测试车辆和嘈杂的现实世界数据,应用了由低成本低精密惯性传感器组成的具有挑战性的条件实时飞行实验。将实现的实验准确性与其他方法所取得的结果进行比较,我们提出的方法验证了显着的改进。

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