<title>Ionospheric distortion mitigation techniques for over-the-horizon radar</title>

Abstract

High-Frequency radar detects targets at thousands of kilometers over the horizon by refracting its beam from the ionosphere. A disturbed ionosphere may distort the signal severely, especially in auroral and equatorial regions. The powerful ground clutter spreads in doppler and masks targets. This distortion is sometimes assumed to take the form of a random complex time-varying distortion function multiplying the time-domain signal. Simple and effective techniques have been developed to mitigate this distortion provided that either the amplitude or the phase of the distortion predominates. The general case of severe amplitude and phase distortion is much more difficult. The techniques are highly model-dependent but are sometimes reasonable for HF radar signals. An emphasis is placed on making the algorithms efficient, so that they can run in real time and keep up with the flood of radar data. The distortion model is first analyzed by phase-screen concepts that model the physics of the electromagnetic propagation through the turbulent ionosphere. To date the techniques have been tested on simulations, since the I/Q data collected thus far do not exhibit the kind of distortions for which these techniques are applicable.

Signal model and detection performance for MIMO-OTH radar with multipath ionospheric propagation and non-point targets

“…where f mnkl denotes the Doppler frequency of the target associated with the mnkl-th path and ϕ mnkl represents the effect of the phase perturbation [19] introduced by the ionosphere reflection which is assumed to be constant during the observation interval. τ Fmk (x Tm , y Tm , γ, β), the time delay of the signal propagating from the mth transmitter to the scatter located at (γ, β) via the kth forward path, and τ Bmnl (x Rn , y Rn , γ, β), the time delay of the mth transmitted signal propagating from the scatter at (γ, β) to the nth receiver via the lth backward path, can be computed via the MQP model [3].…”

<title>Autocalibrating the Wink 2D random array</title>

“…It is hoped that the elevation discrimination provided by the Wink array will assist in understanding and mitigating SDC. (For more on SDC, see [3].) Some initial data were collected, but a proper hardware calibration has yet to be performed.…”