Multiple frequency global navigation satellite system (GNSS) has become more complex due to the existence of extra channels. Typically, auxiliary methods are used to synchronize the second signals at other bands by aiding the acquired channel parameters. However, there are critical limitations because the reception of GNSS signals is subject to uncertainties due to noise carrier injection or circuit interference. The relationship between the two Doppler frequencies can be affected by uncertainties. Therefore, we aimed to implement an efficient dual-frequency field-programmable gate array (FPGA), performing a direct aid tracking method for the secondary channel to achieve resource efficiency and inner aid robustness. A robust estimator that directly links two loops in the two bands is proposed. In this scheme, (1) a robust estimator able to cope with uncertainty; (2) a primary tracking scheme to obtain the error boundary, and (3) a tracked bit-boundary for the initial code phase of the second channel are used. Based on experiments on the FPGA, the robust channel link can achieve direct aid tracking, and 31.02% of the original hardware resources from the aided acquisition module were released satisfactorily.

Due to the rapid development in semiconductor technology, MEMS (microelectromechanical system)-based sensors have been widely used in commercial and military applications. These sensors are characterized by their small size, low cost, low power consumption. However, MEMS sensors are noted for their low accuracy, limiting the applicability in high precision navigation and control. In this paper, a MEMS gyroscope array is developed to account for the limitation of an individual sensor. The key is to develop a robust M-estimation filter to process the sensor array data in real time so as to provide a more accurate estimate of the angular rate, to render fault tolerance capability, and to facilitate signal quality index for system integration. The robust M-estimation Kalman filter is implemented in a DSP/FPGA platform to account for random bias and random walk. In addition, the M-estimation technique is used to accommodate outliers. Allan variance and FFT method are employed as an analyzing tool to quantify the performance. It is verified that the proposed robust M-estimation filter is capable of suppressing non-Gaussian impulse noise and providing a high-accuracy angular rate measurement.