Radar Resource Management for Active Tracking Using Split-Aperture Phased Arrays

Flexible front-end technology will become available in future multifunction radar systems to improve adaptability to the operational theatre. A potential concept to utilize this flexibility is to subdivide radar tasks spatially over the array, the so-called split-aperture phased array (SAPA) concept. As radars are generally designed for their worst-case scenario, e.g., small targets at a large range, the power-aperture budget can be excessive for targets that do not fall within that class. To increase efficiency of the time budget of the radar front-end, the SAPA concept could be applied. In this paper, the SAPA concept is explored to assign radar resources for active tracking tasks of many targets. To do so, we formulate and solve the radar resource management problem for the SAPA concept by employing the quality of service based resource allocation model (Q-RAM) framework. It will be demonstrated by a simulation example that a radar can maintain a larger numbers of active tracking tasks when using the SAPA concept compared to the case that only the full array can be used per task.

Kernel Design Meets Clutter Cancellation for Irregular Waveforms

Efficient clutter filtering for pulsed radar systems remains an open issue when employing pulse-to-pulse modulation and irregular pulse interval waveforms within the coherent processing interval. The range and Doppler domain should be jointly processed for effective filtering leading to a large computational overhead. In this paper, the joint domain filtering is performed by constructing a clutter projection matrix, also known as the projected non-identical multiple pulse compression (NIMPC) method. The paper extends the projected NIMPC filter to irregular pulse interval waveforms. Additionally, a kernel-based regularization will be introduced to tackle the ill-conditioning of the matrix inverse of the NIMPC method. The regularization is based on a model of the second-order statistics of the clutter. Moreover, a computationally efficient algorithm is formulated based on fast Fourier transforms and the projected conjugate gradient method. Through a Monte Carlo study it is demonstrated that the proposed kernelized filtering outperforms the projected NIMPC in clutter filtering.