Non-uniform constrained optimization of radar search patterns in direction cosines space using integer programming

Briheche, Yann; Barbaresco, Frédéric; Bennis, Fouad; Chablat, Damien; Gosselin, F.

doi:10.1109/irs.2016.7497343

Cited by 10 publications

(10 citation statements)

References 12 publications

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“…The set representation of a cover defined by corners (m 0 , n 0 ) and (m 1 , n 1 ) is: C = {g m,n , (m, n) ∈ [m 0 , m 1 [×[n 0 , n 1 [} For example, in cover C 7 (Fig. 2), the corners are (m 0 , n 0 ) = (0, 1) and (m 1 , n 1 ) = (1,2).…”

Section: Definitionmentioning

confidence: 99%

“…Those new possibilities requires a more sophisticated formulation for the optimization problem of the radar search pattern. Approximation of radar search pattern optimization as a set cover problem offers a flexible yet powerful formulation [2], capable of accounting radar specific constraints (localized clutter, adaptive scan-rate updates, multiple missions) without changing the underlying mathematical structure of the optimization problem.…”

Section: Introduction and Contextmentioning

confidence: 99%

See 1 more Smart Citation

Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns

Briheche

Barbaresco

Bennis

et al. 2020

Nature-Inspired Methods for Metaheuristics Optimization

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Section: Definitionmentioning

confidence: 99%

Section: Introduction and Contextmentioning

confidence: 99%

Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns

Briheche

Barbaresco

Bennis

et al. 2020

Nature-Inspired Methods for Metaheuristics Optimization

Self Cite

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“…It turns out that the latter convergence property allows deriving a fixed criterion for the optimization of the update rate. 5…”

Section: Linear Kalman Filtermentioning

confidence: 99%

“…And notably as the radar beam is needed for both TWS and AT, its update frequency for active tracking has to be carefully controlled, as well as for the TWS task. Optimization to cover the entire space during TWS has already been studied, see [4], [5]. In the case of AT, the illuminations requested have a label containing a duration and an update period, and the adaptive resource manager uses a scheduling algorithm to determine in which order these illuminations should be scheduled.…”

Section: Introductionmentioning

confidence: 99%

Fully adaptive update rate for non‐linear trackers

Pilté

Bonnabel

Barbaresco

2018

IET Radar, Sonar & Navigation

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Modern phased-array multifunction radars have the ability to change or schedule the tasks of the beam in order to accomplish all their missions in an optimized fashion. The resource manager of the radar must then control the update rate of the measurement task of target active tracking, so as to minimize the radar computer load without losing targets. Scarce measurements lead to low radar load, but they also lead to an increased number of illuminations at each measurement epoch to find the target. Based upon this rationale, a sound procedure was proposed by Blackman and Van Keuk to derive an optimal measurement rate. Their optimization criterion is established using a linear Singer target model and a linear Kalman filter. In this paper their method is extended, and we propose a versatile optimal update rate algorithm that is applicable to virtually any nonlinear target model combined with any nonlinear filter able to output an error covariance matrix. This includes EKF, UKF, IMM, and particle filters. For numerical experiments and validation we consider a nonlinear target model based on Frenet-Serret 3D equations, and the tracking is performed by a nonlinear Invariant Extended Kalman Filter (IEKF).

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“…Those approaches, however, do not fully use active radars capabilities to dynamically perform beam‐forming. In [11], we presented an approximation of radar search pattern optimisation as a set cover problem, defining a flexible and powerful framework for solving this problem. In [12], we presented how localised clutter and multiple missions can be integrated into the optimisation problem formulation without changing its mathematical structure.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of radar search patterns in localised clutter and terrain masking under direction‐specific scan update rates constraints

Briheche

Barbaresco

Bennis

et al. 2018

IET Radar, Sonar & Navigation

Self Cite

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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.

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Non-uniform constrained optimization of radar search patterns in direction cosines space using integer programming

Cited by 10 publications

References 12 publications

Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns

Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns

Fully adaptive update rate for non‐linear trackers

Optimisation of radar search patterns in localised clutter and terrain masking under direction‐specific scan update rates constraints

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