Abstract-A goal in the software radio design philosophy is to place the analog-to-digital converter as near the antenna as possible. This objective has been demonstrated for the case of a single input signal. Bandpass sampling has been applied to downconvert, or intentionally alias, the information bandwidth of a radio frequency (RF) signal to a desired intermediate frequency.The design of the software radio becomes more interesting when two or more distinct signals are received. The traditional approach for multiple signals would be to bandpass sample a continuous span of spectrum containing all the desired signals. The disadvantage with this approach is that the sampling rate and associated discrete processing rate are based on the span of spectrum as opposed to the information bandwidths of the signals of interest.Proposed here is a technique to determine the absolute minimum sampling frequency for direct digitization of multiple, nonadjacent, frequency bands. The entire process is based on the calculation of a single parameter-the sampling frequency. The result is a simple, yet elegant, front-end design for the reception and bandpass sampling of multiple RF signals. Experimental results using RF transmissions from the U.S. Global Positioning System-Standard Position Service (GPS-SPS) and the Russian Global Navigation Satellite System (GLONASS) are used to illustrate and verify the theory.
Low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. GNSS provides positioning, navigation and timing solutions to users worldwide. However, signal attenuation, reflections or blockages may give rise to positioning difficulties. As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time. In addition, video cameras also possess great potential as alternate sensors in the navigation community, particularly in challenging GNSS environments and are becoming more common as options in vehicles. Aiming at taking advantage of these existing onboard technologies for ground vehicle navigation in challenging environments, this paper develops an integrated camera/IMU/GNSS system based on the extended Kalman filter (EKF). Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment. The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.
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