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In this paper an efficient wideband Direction of Arrival (DoA) and frequency estimation technique is presented which uses only a single snapshot from a multi-beam antenna array. The simultaneous spatial beams (beamspace) are created by a Rotman lens which is composed of a free-space true time delay network attached to a Uniform Linear Array (ULA) of broadband Vivaldi antenna elements. The proposed technique uses machine learning techniques to establish an optimal Neural Network (NN) configuration obtained with a training set. To improve the spatial and frequency space resolution a further estimation stage follows to the NN topology. This results in a lower computational load during the training phase and finally a very fast estimation of direction and frequency of the impinging signal. The performance is evaluated by measurements obtained in an anechoic chamber.

Distributed sensor/radar networks offers several benefits over monostatic systems. For instance, as targets are observed from different aspects angles the detection performance and estimation accuracy of position and speed (Doppler) is improved. Furthermore, a sensor network is able to cope with an increased number of targets. Due to this the interest in such systems has increased over the past years. Moreover, steep improvements in technology and data processing have generated new aspects and fields of application to emerge.

A surveillance systems based on passive radar technique with distributed node has several advantages compared to traditional monostatic radar systems. For instance, a target will be illuminated and observed from different aspects angles. This improves the detection performance and the range/Doppler estimation. A distributed network topology of the surveillance system allows to increase the number of targets which the system can manage simultaneously. From the operators point of view a passive system has the advantage that no electromagnetic energy is emitted and, hence, there is no hassle with any frequency license and the power consumption is low. Investigations have shown that digital broadcast communication stations like DAB-T or DVB-T are very promising illuminator of opportunity for such a system due to their signal modulation characteristic and their possibility to establish a single frequency distributed radar network. An l1 minimization approach is described within this paper for a single frequency passive radar surveillance network. In real scenarios most objects are spread out over several range/Doppler cells due to their size and/or owing to their motion over the coherent integration time. The presented approach is enhanced in such a way that measurements inaccuracies and the effects of extended targets are taken into account.

Passive radar networks offer several advantages compared to standard single radar. For instance a target is illuminated from different aspect angles and this leads to an increase of the detection performance and higher position estimation accuracy. Beside these both improvements the network is able to cope with an increased number of targets and the range/Doppler resolution is enhanced. As the system does not emit any electromagnetic energy the operator has no hassle with a frequency license. A very promising constellation of such a passive radar network uses digital broadcast communication stations like DAB-T or DVB-T which forms a single frequency network. This paper describes a new approach for evaluating data from a passive Multiple-Input-Single-Output (MISO) radar network based on single frequency network (SFN) by employing group sparsity techniques known from Compressive Sensing. When real targets are huge airplanes, observed during their take-off and touchdown phase, the received echoes are spread out over several range/Doppler cells. The proposed methods takes this into account.