Passive radar is an interesting approach in the context of non-cooperative target detection. Because the signal source takes advantage of the so-called illuminator of opportunity (IoO), the deployed system is silent, allowing the operator cheap, portable, and practically undetectable deployments. These systems match perfectly with the use of antenna arrays to take advantage of the additional gains provided by the coherent combination of the signals received at each element. To obtain these benefits, linear processing methods are required to enhance the system’s performance. In this work, we summarize the main beamforming methods in the literature to provide a clear picture of the current state of the art. Next, we perform an analysis of the benefits and drawbacks and explore the chance of increasing the number of antenna elements. Finally, we identify the major challenges to be addressed by researchers in the future.

Knowledge of the spatial and temporal variability of near-surface water vapor is of great importance to successfully model reliable radio communications systems and forecast atmospheric phenomena such as convective initiation and boundary layer processes. However, most current methods to measure atmospheric moisture variations hardly provide the temporal and spatial resolutions required for detection of such atmospheric processes. Recently, considering the high correlation between refractivity variations and water vapor pressure variations at warm temperatures, and the good temporal and spatial resolution that weather radars provide, the measurement of the refractivity with radar became of interest. Firstly, it was proposed to estimate refractivity variations from radar phase measurements of ground-based stationary targets returns. For that, it was considered that the backscattering from ground targets is stationary and the vertical gradient of the refractivity could be neglected. Initial experiments showed good results over flat terrain when the radar and target heights are similar. However, the need to consider the non-zero vertical gradient of the refractivity over hilly terrain is clear. Up to date, the methods proposed consider previous estimation of the refractivity gradient in order to correct the measured phases before the refractivity estimation. In this paper, joint estimation of the refractivity variations at the radar height and the refractivity vertical gradient variations using scan-to-scan phase measurement variations is proposed. To reduce the noisiness of the estimates, a least squares method is used. Importantly, to apply this algorithm, it is not necessary to modify the radar scanning mode. For the purpose of this study, radar data obtained during the Refractivity Experiment for H ${}_2$ O Research and Collaborative Operational Technology Transfer (REFRACTT_2006), held in northeastern Colorado (USA), are used. The refractivity estimates obtained show a good performance of the algorithm proposed compared to the refractivity derived from two automatic weather stations located close to the radar, demonstrating the possibility of radar based refractivity estimation in hilly terrain and non-homogeneous atmosphere with high spatial resolution.

Passive radar has drawn a lot of attention due to its applications across military and civilian sectors. Under this working paradigm, the utilization of antenna arrays is instrumental, as it increases the signal quality and enables precise target positioning. These promising features rely, however, on the precise calibration of the antenna array, as the different hardware components introduce impairments that compromise the beamforming capabilities of the system. We propose a technique that employs a low-power external beacon signal to produce precise information about the target location, avoiding the angular ambiguities present in other solutions in the literature. The experimental results demonstrate the method’s ability to effectively correct the amplitude and phase inconsistencies while compensating for frequency drifts, enabling beamforming capabilities and direction-of-arrival estimation. Among the tested beacon waveforms, the pseudo-random noise-based signals proved the most robust, especially in low-power scenarios. Additionally, the method was validated in a passive radar setup, where it successfully detected a vessel using opportunistic signals. These findings highlight the method’s potential to enhance passive radar performance while maintaining a low probability of detection, a key aspect in military applications, as well as its applicability to civilian purposes, such as infrastructure monitoring, environmental observation, and traffic management.

Tropospheric refractivity, which is closely related to temperature, pressure, and relative humidity, is a valuable parameter for weather forecasting and climate analysis. It has already been demonstrated that refractivity estimates can be derived using the phase measurements corresponding to radar signals backscattered from stationary targets over any terrain orography, with high temporal resolution. However, the random distribution of stationary targets affects the spatial resolution provided by the computed refractivity estimates. It is of interest to obtain reliable radar-based refractivity maps to assist final users with data interpretation and analysis, so the use of the a suitable geostatistical interpolation technique to obtain refractivity maps is studied in this paper. Refractivity estimates obtained from C-band radar data gathered during 2019 by the United Kingdom’s national weather service (Met Office) are used to evaluate the accuracy of the method by comparing the results to ground-based weather stations and the ECMWF’s ERA5 reanalysis dataset.

Tropospheric refractivity, related to temperature, pressure and humidity is an interesting parameter for weather analysis, prediction and study of climate trends. It has been shown to be useful for the detection and forecast of convective events. It has already been demonstrated that tropospheric refractivity can be estimated from radar phase measurements. In this paper a non-linear least squares based approach for the estimation of the tropospheric refractivity that simultaneously provides estimates of the refractivity vertical gradient is presented. A significant improvement of the presented technique is that it allows estimation of the refractivity over any terrain orography, flat or hilly. Furthermore, the method developed can be implemented on klystron as well as on magnetron based radars. Results for both radar types, at S- and C-band, located over flat and hilly terrain show the potential of the method.

The aim of this paper is to estimate the atmospheric refractivity and to analyse its statistical properties. Refractivity estimates are obtained from phase measurements of the radar response to stationary targets. The required data are obtained with the C-Band polarimetric weather Radar of Meteogalicia.