This article focuses on the experimental validation of probing signals designed to enable radar operation in spectrally crowded environments using an S-band software defined radar (SDR). The tested waveforms ensure spectral coexistence between the sensing system and frequency-overlaid emitters, while optimizing radar performance. This is achieved through a bespoke notching of the radar signal spectrum to control the amount of interference injected by the radar in each shared frequency interval. In addition, some relevant features of the probing signal that influence radar performance are controlled via a similarity index, describing the maximum allowable distance between the spectrally notched waveform and a prescribed radar signal. In a first stage, the study is aimed at verifying whether the transmit and receive chains of the SDR system impair the spectral and temporal features of the designed waveforms. Subsequently, the tested signals are radiated into the environment to investigate their effectiveness to detect targets in the illuminated scene, as well as to ensure spectral coexistence in the presence of frequency-overlaid emitters. The results demonstrated that by exploiting the designed radar probing signals, the SDR system is capable of sharing spectrum with other radio frequency wireless systems while also allowing to detect both stationary and moving targets.

The design of bespoke adaptive detection schemes relying on the joint use of multistatic/polarimetric measurements requires a preliminary statistical inference on the clutter interference environment. This is of paramount importance to develop an analytic model for the received signal samples, which is mandatory for the synthesis of radar detectors. In this respect, the aim of this article is the development of suitable learning tools to study some important statistical features of the sea-clutter environment perceived at the nodes of a multistatic/polarimetric radar system. Precisely, the stationarity of the data in the slow-time domain is first assessed by resorting to generalized inner product (GIP) based statistics. Then, the possible presence of structural symmetries in the clutter covariance matrices is investigated. Finally, relationships between some statistical parameters characterizing the sea-clutter returns on the bistatic polarimetric channels are explored via specific sequential hypothesis testing. This research activity is complemented by the use of radar returns measured via the netted RADar (NetRAD), which collects simultaneously monostatic and bistatic polarimetric measurements. The results indicate that the analyzed data can be modeled as drawn from a stationary Gaussian process within the coherence time. In addition, the bistatic returns on the different polarimetric channels can be assumed statistically independent with speckle components possibly exhibiting proportional/equal covariance matrices depending on the transmit/receive polarization and bistatic geometry.

This article deals with the statistical inference of simultaneously recorded co- and cross-polarized bistatic coherent sea-clutter returns at S-band. This study is conducted employing appropriate statistical learning tools, involving the complex envelope of data, to assess the compliance of the available measurements with the spherically invariant random process (SIRP) representation, as well as to analyze possible texture correlations among the diverse polarimetric channels. Moreover, the spatial heterogeneity of the sea-clutter data is studied. The results highlight that the SIRP model is a good candidate for the representation of bistatic coherent clutter and usually the coherence time of the SIRP texture at the bistatic nodes is longer than that in the monostatic sensing. Notably, at bistatic angles in order of 60°, the quadrature components of the cross-polarized bistatic measurements substantially exhibit a Gaussian behavior. These achievements further shed light on the bistatic sea-clutter diversity from the geometric and polarimetric point of view.

This paper investigates the possibility of transmitting waveforms designed to enable spectral coexistence between radar and other Radio Frequency (RF) wireless systems via a Software Defined Radar (SDR). The design technique tested in this study nominally enables the placement of notches in the spectrum of the synthesized probing radar signal. Their widths and depths are set during the design stage so as to accounting for the interference into each shared frequency interval, allowing for spectral coexistence. At the assessment stage, the synthesized signal is used with the PARSAX radar system, an SDR capable of operating in the S frequency band. The analysis first focuses on studying the compliance of the signal generated by the PARSAX radar with its theoretical counterpart. Subsequently, open-air experiments are conducted in the presence of stationary and moving targets. The results show that the spectral characteristics of the probing radar signal adhere well to the theoretical spectral mask, and prove the system ability to detect both stationary and moving targets.

The design of bespoke adaptive detection schemes relying on the joint use of multistatic/polarimetric measurements requires a preliminary statistical inference on the clutter interference environment. This is fundamental to develop an analytic model for the received signal samples, which is used to synthesize the radar detector. In this respect, the aim of this paper is the design of suitable learning tools to study some important statistical properties of the sea-clutter environment perceived at the nodes of a multistatic/polarimetric radar system. The study is complemented by the use of radar returns measured with the Netted RADar (NetRAD), which collects simultaneously monostatic and bistatic measurements. Precisely, the homogeneity properties of the data in the slow-time domain are first assessed resorting to Generalized Inner Product (GIP) based statistics. Then, the possible presence of structures in the clutter covariance matrices (both inter and intra channels) is investigated through ad-hoc statistical tools. The results show that the data, regardless the polarimetric/geometric configuration, can be modeled as drawn from a stationary process within the coherence time. Moreover, for both the monostatic and the bistatic returns the structure of the covariance matrix depends upon the polarimetric/geometric configuration of the sensing system.