Using VHF Navigation Aid to Estimate Radar Cross Section of Airliners by Inverting the Radar Equation

Abstract

The VHF Omnidirectional Range (VOR) navigation aid system can be used as donor of signal for passive radar experiments. An experiment was set up close to a VOR beacon to detect airliners and to analyze their radar cross section in function of their attitude. This paper presents the configuration of the trials, the adapted signal processing and the detection results obtained for one of the detected targets. The radar cross section (RCS) was estimated from the computation of power budget in radar equation. An analysis of the evolution of the RCS is given in function of the attitude of this detected target in azimuth and elevation. Mean values of RCS for many models of airliners detected during the trial campaign and deduced by the same approach are then presented.

Analysis of airplane signatures using passive VHF radar for recognition perspectives

“…3 shows the histograms of bistatic RCS of the airplanes models (small, medium and large) for HH (φ-φ) and HV (φ-θ) cases and for all scattered and incident angles considered lines above. We can see that most of the RCS values are concentrated between 0 dBsm and 40 dBsm (in accordance with [3], Airbus 321 is comparable to the model of medium size used lines above). Most importantly, in global, RCS values between the airplanes are very similar, making the discrimination difficult from RCS only.…”

“…V. CONCLUSIONS AND PERSPECTIVES It has been shown, through simulations, that, globally, the bistatic RCS of airplanes of different sizes (large, medium and small) are similar, which makes difficult their discrimination on the sole basis of RCS value. It has been also shown that it is more sensible to look for the angular frequency (extraction of this information can be done as in [3]) of the bistatic RCS when the airplane is moving in order to discriminate the planes by sizes (larger planes will scatter power with finer and more peaks). These simulation results, can be used to synthesize bistatic RCS evolution for arbitrary trajectories (for example based on real trajectories) and include many VORs and different positions of receiver, in order to consider multiple scenarios based on real data to train neural networks (which need a large collection of data that many times are not available by measurement) on the recognition of airplanes.…”

“…In this band the wavelength is about 2.6 m which means that the size of commercial airplanes is on the order of tens of wavelengths. By exploiting the Automatic Dependent Surveillance-Broadcast (ADS-B) data broadcasted (L band) by the airplanes, the position of the airplane can be known and its radar cross section (RCS) can be obtained, as in [3], and it is of the order of tens of dBsm. Work has also been done to classify, according to size (small, medium, large), the airplanes by exploiting an aggregation of RCS of the airplanes [4] [5].…”

Generalized Radar Range Equation Applied to the Whole Field Region

“…As for the radar range equation, it can be applied to estimate the radar cross section (RCS), but the RCS is estimated in the far field [18]. The unique RCS characteristic is calculated to predict a target state based on the radar equation for 26 GHz and 79 GHz short range vehicular radars.…”

Deep learning for aircraft classification from VHF radar signatures

“…Although ground reflection interference [9] (which may also interfere with the noise at the receiver), wave polarization [8] and aeroplane heading [10] can make extremely difficult the extraction of these values in some particular cases. By exploiting the automatic dependent surveillance‐broadcast (ADS‐B) data broadcasted (L band) by the aeroplanes, their position and heading are then known and their BiStatic‐RCS (BS‐RCS), that is, RCS for separated transmitter and receiver, can be effectively estimated as in [5].…”