This report introduces inertial navigation and its error characteristics, focusing on strapdown systems using MEMS devices. It highlights the limitations of current MEMS-based inertial navigation systems (INSs), which, despite improvements in sensor performance, still suffer from significant drift. A simple INS based on the Xsens Mtx inertial measurement unit (IMU) was analyzed, showing that position error grows to over 150 meters after 60 seconds of operation. The primary cause of this drift is the propagation of orientation errors due to noise in gyroscope signals. White noise is identified as the main contributor to this drift. Sensor fusion and domain-specific constraints can reduce drift. For example, using magnetometers in an INS can reduce position error from over 150 meters to around 5 meters after 60 seconds. The report also discusses the error characteristics of MEMS gyroscopes and accelerometers, including constant bias, thermo-mechanical white noise, flicker noise, temperature effects, and calibration errors. These errors affect the accuracy of the INS, with angle random walk and uncorrected bias errors being the most significant for MEMS devices. The report describes the Allan Variance technique for analyzing noise in gyroscope and accelerometer signals. This technique was applied to the Xsens Mtx device, revealing the presence of white noise and bias instability. The results showed that the z-axis accelerometer had a higher velocity random walk than the x and y-axis accelerometers. The strapdown inertial navigation algorithm was detailed, showing how errors in gyroscopes and accelerometers propagate through the system. The algorithm was implemented and tested on real data, demonstrating significant drift in position after 60 seconds. The drift was primarily attributed to orientation errors, with accelerometer noise having a negligible effect after a few seconds of operation. The report concludes that while MEMS IMU technology is improving, it is not yet possible to build a MEMS-based INS with sub-meter accuracy for more than one minute of operation.This report introduces inertial navigation and its error characteristics, focusing on strapdown systems using MEMS devices. It highlights the limitations of current MEMS-based inertial navigation systems (INSs), which, despite improvements in sensor performance, still suffer from significant drift. A simple INS based on the Xsens Mtx inertial measurement unit (IMU) was analyzed, showing that position error grows to over 150 meters after 60 seconds of operation. The primary cause of this drift is the propagation of orientation errors due to noise in gyroscope signals. White noise is identified as the main contributor to this drift. Sensor fusion and domain-specific constraints can reduce drift. For example, using magnetometers in an INS can reduce position error from over 150 meters to around 5 meters after 60 seconds. The report also discusses the error characteristics of MEMS gyroscopes and accelerometers, including constant bias, thermo-mechanical white noise, flicker noise, temperature effects, and calibration errors. These errors affect the accuracy of the INS, with angle random walk and uncorrected bias errors being the most significant for MEMS devices. The report describes the Allan Variance technique for analyzing noise in gyroscope and accelerometer signals. This technique was applied to the Xsens Mtx device, revealing the presence of white noise and bias instability. The results showed that the z-axis accelerometer had a higher velocity random walk than the x and y-axis accelerometers. The strapdown inertial navigation algorithm was detailed, showing how errors in gyroscopes and accelerometers propagate through the system. The algorithm was implemented and tested on real data, demonstrating significant drift in position after 60 seconds. The drift was primarily attributed to orientation errors, with accelerometer noise having a negligible effect after a few seconds of operation. The report concludes that while MEMS IMU technology is improving, it is not yet possible to build a MEMS-based INS with sub-meter accuracy for more than one minute of operation.