The Automated Radio Measurement System (ARMS) developed by EMA is used to measure the broadband performance of RF transmitters and receivers. To accurately predict RF interference (RFI) between multiple RF systems in complex environments, one must use high fidelity measured data. There are many ways that transmitters can interfere with receivers. The root cause of the interference is often not intuitive and the limited performance data provided by specification sheets or DD 1494s makes developing accurate models a challenge.
Analysts must often make educated guesses or use worst-case assumptions in their RFI analysis, resulting in missing real interference problems or over-engineering the solution for interference problems that do not exist. This approach has major implications on time/resource allocations that can result in overly complicated equipment. Unfortunately, measured data, especially broadband measured data, is not readily available for most RF systems. Manufacturers are required to demonstrate that their equipment meets the various military and commercial standards with respect to transmitter emissions and receiver susceptibility. However, the raw measured data is almost never made available to the analyst. Even when such data can be obtained, it very often lacks the fidelity necessary for RF interference analysis.
With the ARMS, EMA can rapidly and accurately characterize the performance of RF systems. Automation and high dynamic range measurements are key aspects of the ARMS. For transmitters, the ARMS measures the fundamental, harmonics and spurious emissions. For receivers, mixer product responses and spurious responses of the receiver are captured using susceptibility metrics such as SINAD, BER, and C/No. Data measured by ARMS can be directly used in the Ansys EMIT technology for RFI simulations.
An example of measured transmitter data collected with the ARMS for a UHF radio is shown below. A parametric model for the transmitter based upon specification sheet information is overlaid to show the important differences between parametric models and measured data. One can clearly see numerous spurious emissions that occur both below and above the fundamental frequency. The parametric model has no knowledge of the spurious emission frequencies. Typically, a specification sheet will provide a maximum spurious emission amplitude limit, but specific frequency information is not provided. It can also be seen that the harmonic amplitudes based upon specification sheet information are far different from the measured amplitudes of the harmonics. As shown in the image, spec sheet harmonic amplitudes can be inaccurate by as much as 50 dB in this example. Clearly, the missing spurious emissions and the gross inaccuracy of the harmonic amplitudes could lead to very inaccurate RF interference predictions.
Receiver susceptibility is measured by injecting the intended signal into a receiver to establish a baseline performance metric such as SINAD or C/No. Then, an interfering signal is simultaneously introduced, and the performance metric is monitored for degradation. The interfering signal is swept both in frequency and amplitude to establish the broadband susceptibility of the receiver. The detailed frequency-dependent susceptibility profile is not available from a manufacturer’s specification sheet. The ARMS Rx measurement system automates and accelerates the Rx measurement process to rapidly and accurately create models for RF interference analysis.
Measured receiver susceptibility data for a GNSS receiver tuned to L1 is shown below. The measured data is the red trace while a parametric model based upon the spec sheet is the black trace. There are two important observations to make about this figure. First, the receiver in-band susceptibility is much broader than the stated selectivity data provided on the spec sheet. In fact, the spec sheet selectivity is inaccurate by as much as 60 dB. Second, there is a spurious response of the receiver near 1280 MHz. This response is not the L2 response of the receiver and the specification sheet for the receiver provides no information about this response.
Using ARMS data in your RFI simulations dramatically reduces the number of predicted false positive interference problems and identifies real interference problems that would have otherwise been missed. ARMS helps to reduce expensive testing and find RFI problems early in the design cycle when they can be optimally addressed.
To learn more about how EMA can help your organization with RF measurements, Contact Us.