Svante Björklund

The presence and importance of small drones (unmanned aerial vehicles) are increasing. With traditional radar detection the target radar cross section and its statistical properties are needed in order to design the detector and to assess detection performance. The authors make a statistical analysis of the radar cross section of two small fixed‐wing drones using measured data from typical flights. Using data from a typical flight instead of measurements or electromagnetic calculations as a function of aspect angle is a convenient method. Work results of the authors in mean values, medians and standard deviations, in analyses of common target probability distributions and in decorrelation times for the radar cross section, are necessary for design and assessment of the detection performance. The authors suggest a new target probability distribution which agrees better with the measured data than the others tested. The authors also note significant differences between the drones.

We have designed an experiment for low-cost indoor measurements of rank and other properties of direct and scattered signals with radar interference suppression in mind. The signal rank is important also in many other applications, for example, DOA (Direction of Arrival) estimation, estimation of the number of and location of transmitters in electronic warfare, and increasing the capacity in wireless communications. In real radar applications, such measurements can be very expensive, for example, involving airborne radars with array antennas. We have performed the measurements in an anechoic chamber with several transmitters, a receiving array antenna, and a moving reflector. Our experiment takes several aspects into account: transmitted signals with different correlation, decorrelation of the signals during the acquisition interval, covariance matrix estimation, noise eigenvalue spread, calibration, near-field compensation, scattering in a rough surface, and good control of the influencing factors. With our measurements we have observed rank, DOA spectrum, and eigenpatterns of direct and scattered signals. The agreement of our measured properties with theoretic and simulated results in the literature shows that our experiment is realistic and sound. The detailed description of our experiment could serve as help for conducting other well-controlled experiments.

Safety and security applications benefit from better situational awareness. Radar micro‐Doppler signatures from an observed target carry information about the target's activity, and have potential to improve situational awareness. This article describes, compares, and discusses two methods to classify human activity based on radar micro‐Doppler data. The first method extracts physically interpretable features from the time‐velocity domain such as the main cycle time and properties of the envelope of the micro‐Doppler spectra and use these in the classification. The second method derives its features based on the components with the most energy in the cadence‐velocity domain (obtained as the Fourier transform of the time‐velocity domain). Measurements from a field trial show that the two methods have similar activity classification performance. It is suggested that target base velocity and main limb cadence frequency are indirect features of both methods, and that they do often alone suffice to discriminate between the studied activities. This is corroborated by experiments with a reduced feature set. This opens up for designing new more compact feature sets. Moreover, weaknesses of the methods and the impact of non‐radial motion are discussed.

Terrain‐scattered interference (TSI), that is, jammer signals reflected on the earth's surface, is a significant problem to military airborne radar. In auxiliary beam TSI suppression, the TSI in the main radar beam is estimated by a single or several auxiliary beams and is subtracted from the main beam channel. The signal to subtract is the auxiliary beam signals fed through an estimate of the ‘reflection system’, which describes scattering on the surface. The authors first present results on the structure of this TSI suppression, on the estimation of the reflection system and on the quality of the estimate. Then the authors derive theoretical expressions for the signal‐to‐interference plus noise ratio (SINR) and the remaining TSI power for a single auxiliary beam. Since the SINR is directly connected to the radar performance, it can be seen what factors affect the performance and how. It was noted that when the estimated reflection system is missing one or more delays of the true system, the TSI filter cannot suppress the TSI signal completely. This phenomenon, which is called ‘TSI leakage’, has a very large impact on the performance. The SINR cannot be kept constant. Instead, an ‘SINR improvement’ can be defined.