In continuous-wave (CW) radar systems, such as frequency-modulated (FMCW), frequency-stepped (FSCW), or orthogonal frequency-division multiplexing (OFDM) radar systems, the range and velocity uncertainty are significantly impaired by phase noise decorrelation. Therefore, radar designers require accurate knowledge of their synthesizers' phase noise profiles to assess and predict radar performance. However, commercial phase noise analyzers cannot determine phase noise during modulation, and this may differ notably from phase noise in the pure CW mode. Recent methods for FMCW phase noise analysis usually require comprehensive a priori knowledge of modulation parameter and are prone to systematic deviations. To overcome these issues, we propose a new approach based on differential analysis of subsequent time-domain measurements. This method retains, statistical phase noise information while reducing systematic influences. For the first time, less a priori signal knowledge is required, and the method works for nearly any kind of broadband signal modulation. The concept requires only a digitizer (e.g., an oscilloscope) and some digital signal processing. The proposed method is first experimentally tested with different phase-locked-loop (PLL)-based synthesizer phase noise profiles. The obtained phase noise profiles agree perfectly with the results of an established measurement system. After this proof of basic functionality, the unique phase noise analysis capability for BB modulated signals is demonstrated with PLL-generated FMCW signals. The results reveal a significant phase noise difference between the different setups and clearly show the capability and benefit of the novel phase noise spectral density measurement concept.

In radar and communication systems, phase noise is one of the main causes of performance degradation. Phase noise increases the uncertainty in radar measurements and limits the achievable data rates in communication systems. When radio frequency (RF) signals distorted by bandlimited phase noise are modeled, the phase noise power spectral density (PSD) is usually approximated by a scalar root-meansquare (RMS) phase error derived from a simple integration of the PSD. This disregards the close-to-carrier noise excess. In this article, we show that this convention simplification describes the real behavior of phase noise inadequately. In addition, we present a simulation of realistic phase noise behavior. The novel additive colored noise (ACN) model requires a representative phase noise PSD of the phase-locked loop (PLL) signal generator phase noise to be modeled. The developed ACN approach is validated by comparing the measured PSD of different PLL signal generators with the respective simulated RF signals distorted by phase noise. As a simple metric for assessing the quality of the phase noise models, we use the influence of phase noise on carrier frequency estimation. It is shown that the novel approach shows a significant improvement in the agreement between the simulated and measured precision compared to standard additive white Gaussian noise (AWGN) models. The results show that advanced phase noise models, as proposed in this paper, are necessary to adequately model and predict the performance of radar and communication systems.

In coherent continuous-wave (CW) radar systems, such as frequency-modulated CW (FMCW) radar systems, measurement precision is distorted by phase noise and systematic phase errors during radar signal generation. Unfortunately, to date, these phase distortions have typically been modeled based on many simplifications, often leading to overoptimistic predictions of radar performance. For example, phase noise is regularly considered based on additive white Gaussian noise (AWGN) models, ignoring its colored spectral property. Systematic errors are also frequently not properly separated from stochastic distortions, or not precisely measured and modeled. To overcome these issues, an advanced CW radar and frequency synthesizer model is proposed, analyzed, and experimentally verified in this paper. It is shown how systematic phase errors can be measured and how the influence of phase distortions on radar measurement can be accurately predicted. The proposed modeling approach was investigated for range correlation and range precision. The key metrics are the phase noise power spectral density (PSD) for range correlation and the variance of the frequency estimate for range precision. A 24-GHz FMCW radar system was used for the experimental verification. Long-range radar targets with distances of 38 to 880 m were created with an analog fiber optical link. Therefore, a precise and systematic evaluation of all effects was possible. The proposed phase distortion modeling approach realistically represented the range-dependent radar measurement effects. This enables the most precise simulation of and prediction for FMCW radar performance to date.INDEX TERMS Noise analysis, noise modeling, range correlation, range precision, FMCW radar.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.