No abstract

Thales new generation digital multi-missions radars, fully-digital and software-defined, like the Sea Fire and Ground Fire radars, benefit from a considerable increase of accessible degrees of freedoms to optimally design their operational modes. To effectively leverage these design choices and turn them into operational capabilities, it is necessary to develop new engineering tools, using artificial intelligence. Innovative optimization algorithms in the discrete and continuous domains, coupled with a radar Digital Twins, allowed construction of a generic tool for "search" mode design (beam synthesis, waveform and volume grid) compliant with the available radar time budget. The high computation speeds of these algorithms suggest tool application in a "Proactive Radar" configuration, which would dynamically propose to the operator, operational modes better adapted to environment, threats and the equipment failure conditions.

Modern radars are highly flexible, using digital antennas which can dynamically change the radar beam shape and position through electronic control. Radar surveillance is performed by emitting sequentially different radar beams. Optimization of radar surveillance requires finding, among a collection of available radar beams with different shapes and positions, a minimal subset of radar beams which covers the surveillance space, ensuring detection while minimizing the required scanning time.Optimal radar surveillance can be modelled by grid covering, a specific geometric case of set covering where the universe set is laid out on a grid, representing the radar surveillance space, which must be covered using available subsets, representing the radar beams detection areas. While the set cover problem is generally difficult to solve optimally, certain geometric cases can be optimized in polynomial time.This paper studies the theoretical complexity of grid cover problems used for modelling radar surveillance, proving that unidimensional grids can be covered by strongly polynomial algorithms based on dynamic programming, whereas optimal covering of bidimensional grids is generally non-deterministic polynomially (NP) hard.This work is partly supported by a DGA-MRIS scholarship.

Cognitive radars are systems capable of optimizing emission and processing by exploiting knowledge about environment and operational scenario. Those improvements are achieved by controlling new degrees of freedom offered by modern radar technology. In particular, active phased-array radars can perform bidimensional beam-steering and beam-forming. Those new capabilities allow adaptation of radar search patterns to localized constraints. We present an improvement from our previous set cover approximation for radar search pattern time-budget minimization, accounting for localized constraints of terrain masking and direction-specific scan update rates. The addition of those constraints however does not modify the underlying mathematical structure of the problem, which can be solved using integer programming methods.