MELISSA is a new Ground Based (GB) MIMO SAR system developed by JRC. Despite the successes of MELISSA, GB MIMO SAR sensors could be particularly suitable for many other applications such as MIMO MTI, 3-D ISAR systems and Automatic Target Recognition (ATR) which are still under investigation. This document is addressed for investigating the ability of MELISSA as ATR sensor. The results have proved the abilities of MELISSA of of detecting, discriminating and recognizing potential targets presenting in the area of interest.

One of the most important task for a radar system in the security domain is to discriminate a potential moving target from the clutter background, especially when the Signal-to-Clutter Ratio (SCR) is low. This document hence describes a new algorithm for improving the detection of a potential moving target when a Ground Based (GB) MIMO SAR is adopted. GB-MIMO SAR is a new family of radar sensors which are able to create a 2-D image of a scene of interest without a moving platform. In order to improve the detection ability of the system, especially when SCR is low, a novel signal processing approach is presented, based on the Ground Moving Target Indication (GMTI) architecture. Since a GB-MIMO SAR image is obtained by acquiring data from several channels, it is possible to increase the SCR by adding them coherently and adopting a Single Delay-Line Canceler (SDLC). The results proved the ability of the proposed method for increasing the Signal-to-Clutter Ratio, especially for stationary or quasi-stationary clutter.

The interest in bi- and multistatic radar has been growing over the last years as they offer various advantages. For instance the transmitter and receiver can easily be separated and therefore the survivability can be enhanced by deploying the transmitter further away from the scene. Bistatic radar can provide a cost effective solution as the transmitter and receiver can be built without the cost driving transmit/receive-modules. Beyond this, with large baselines it is likely to increase the signature from stealthy objects. Another interesting aspect of bistatic imaging radar is the reduced dynamic range of difficult imaging environments such as urban ones. However, besides the benefits of bi- and multistatic radar one has to solve several technical problems [1] - such as the synchronisation and determining the relative position and orientation of both platforms towards each other. This paper discusses a method which allows determining these two fundamental parameters of an airborne bistatic SAR system and presents first simulations result, which confirms the method.

In per-survivor processing (PSP), the number of parameter estimators equals the number of retained hypothetical data sequences (survivors). The authors propose an algorithm which uses an arbitrary number of parameter estimators and compromises between the two extremes of tentative decisions (one parameter estimator) and PSP. Specific applications to reduced state sequence estimation (RSSE) and maximum likelihood sequence estimation (MLSE) with adaptive tracking of a fading channel are considered.

We propose a fully parametric, time-discrete, symbol-spaced simulation model of frequency-selective mobile radio channels. The model encapsulates transmission and receiver filters and a physical channel accounting for both Rayleigh and Rice fading. The simulator allows one to accurately trade between complexity and quality of representation of the propagation channel, making use of a digital filter of variable order to implement the Doppler spectrum of the fading processes. Numerical results assessing the performance of the model in terms of the simulation time and the power spectrum of the fading processes are provided. A communication system with an adaptive receiver is employed to test the reliability of the propagation model. Increasing the degree of accuracy in the approximation of the Doppler spectrum, the performance of the considered receiver, expressed in terms of symbol error rate, rapidly converges to a specific function of the signal to noise ratio.