Inertial navigation is a crucial part of vehicle navigation systems in complex and covert surroundings. To address the low accuracy of vehicle inertial navigation in multifaced and covert surroundings, in this study, we proposed an inertial navigation error estimation based on an adaptive neuro fuzzy inference system (ANFIS) which can quickly and accurately output the position error of a vehicle end-to-end. The new system was tested using both single-sequence and multi-sequence data collected from a vehicle by the KITTI dataset. The results were compared with an inertial navigation system (INS) position solution method, artificial neural networks (ANNs) method, and a long short-term memory (LSTM) method. Test results indicated that the accumulative position errors in single sequence and multi-sequences experiments decreased from 9.83% and 4.14% to 0.45% and 0.61% by using ANFIS, respectively, which were significantly less than those of the other three approaches. This result suggests that the ANFIS can considerably improve the positioning accuracy of inertial navigation, which has significance for vehicle inertial navigation in complex and covert surroundings.
A compact eighth-mode substrate integrated waveguide (EMSIW) LTCC filter is proposed, its electrical size is reduced by a complementary split-ring resonator loaded on the EMSIW. The filter is fabricated on multilayer ceramic, which, make the filter further compact. The filter was fabricated and measured. Results show the filter operating at 12.7 GHz with about 20.4% bandwidth. The measured minimum insertion is 1.3 dB, with an attenuation level of more than 25 dB from 4 to 10.0 GHz. The volume of filter is only 5.0 3 5.0 3 0.6 mm 3 .
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