SYSTEM LEVEL RADAR SEARCH OPTIMIZATION

1. 1st Annual Israel Multinational BMD Conference & Exhibition SYSTEM LEVEL RADAR SEARCH OPTIMIZATION Presented By: Ms. A. Gur WALES, Ltd., Israel

2. PRESENTATION TOPICS • Introduction • The Search Patterning Problem • BeamCover Tool Description • Search Pattern Trade-Offs • Summary

3. INTRODUCTION • BMD systems rely on radars to search the sky and detect incoming TBMs • Search performance is key to the effectiveness of the defense system • The definition of the search mission offers interesting tradeoffs between: • Probability of detection (Pd) • Sensor load • Interception battlespace • In order to probe these issues WALES, Ltd. developed the BeamCover search patterning tool

4. BEAMCOVER OVERVIEW • At the heart, BeamCover is a search pattern solver. Given a search mission it attempts to find a satisfying search pattern • BeamCover search patterns are distributions of sensor resources on a single beam resolution • On this engine sits an optimization driver that pushes the search mission to various limits according to optimization requirements

5. THE SEARCH PATTERNING PROBLEM • Given a search mission defined by: • A sample of possible threat trajectories (geometry, RCS) • Timeline constraints • Probability of detection (Pd) requirements • A set of possible radar search beams (direction, waveform) • Resources allocated for search • Find a beam-wise distribution of search resources that provides the required Pd and timeline constraints for all threat trajectories • The challenge here is the large number of trajectories needed to sample the threat (~105 for a large enemy country) and the large number of possible beams (~104) Pd=0.99 Pd=0.9 Elevation Pd=0.85 Azimuth

6. BASIC TOOL PROPERTIES – RADAR, THREAT AND TIMELINE • Geographic location, orientation, radar horizon, etc. • A palette of dwells (waveforms) defined by their power, duration, instrumented range, Pfa, # of range and Doppler gates, and pulse structure • Azimuth X Elevation grid of possible beam directions • Beam amplitude – Gaussian with off boresight effects • Detection process accounts for: • Threat RCS - mean and distribution type • Trajectory path inside beam • Verification process • BeamCover supports a variety of timeline constraints: • Fixed: Detection no later than t seconds prior to threat descending to altitude h • Interceptor time of flight dependent: detection should enable interception at altitude h by at least one firing unit

7. BASIC TOOL PROPERTIES – SEARCH PATTERN SOLVER • We start with a guess search pattern (e.g. random distribution of resources) • Define an objective function that reflects the distance between the required performance and the performance of the current search pattern • A Simulated Annealing solver is used to gradually modify the resource distribution in an attempt to meet the search mission requirements: • Small quantities of radar resources are stochastically moved around until a suitable solution is found • The algorithm uses a combination of “good” (objective function reducing) and “bad” (objective function increasing) moves in a way that converges to a solution while allowing escapes from local minima • Solver is capable of dealing with large problems (hundreds of thousands of trajectories, tens of thousands of beams) within several days, on a desktop PC

8. Legend Simulated Annealing Solver Optimization Driver OPTIMIZATION MODES – RESOURCE REDUCTION • The most straightforward optimization goal is to find a search pattern that satisfies the search mission using the least amount of resources • We do this by reducing the available resources every time the solver succeeds in finding a solution • Initial search mission: • Trajectories • Timeline constraints • Required Pd • Initial guess search resources Succeed Reduce search resources by a small amount Adapt search pattern to restore Pd Find search pattern that satisfies initial mission Succeed Failed Failed Increase search resources Save last valid solution and stop

9. MINIMAL RESOURCES EXAMPLE • Initial mission: provide minimal engagement interval with minimal resources • Result: optimal search pattern, minimal resources required =12.5% Revisit rate

10. Legend Simulated Annealing Solver Optimization Driver OPTIMIZATION MODES – INTERCEPTION WINDOW EXPANSION • Given a fixed amount of resources, find a search pattern that maximizes the interception windows of the different trajectories • We do this by increasing a trajectory’s interception window by a small amount every time the solver succeeds in finding a solution • Initial search mission: • Trajectories • Timeline constraints for minimal interception window • Required Pd • Search resources Succeed • Choose a trajectory whose interception window can be expanded (not limited by horizon, discrimination or interceptor constraints) • Expand trajectory interception window by a small amount Adapt search pattern to restore Pd Find search pattern that satisfies initial mission Succeed Failed Failed Not enough resources Save last valid solution and stop

11. AVAILABLE INTERCEPTION WINDOW EXAMPLE • Allow search resources of 55% • Provide available interception window for a given defense deployment 12.5% Resources 55% Resources Portion of trajectories Interception Altitude Revisit rate

12. ADDITIONAL CAPABILITIES – PERFORMANCE IN A COMPLEX ENVIRONMENT • BeamCover can compute multi sensor search patterns • Radars are straightforwardly added by including their beam set in the search patterning problem • For each part of the threat we can specify the sensor or sensors that should detect it. We thus have the flexibility of designing a mixture of joint and individual detection requirements • Resource constraints can be put on the overall array and on individual sensors

13. ADDITIONAL CAPABILITIES – MONTE CARLO DETECTION SIMULATION • BeamCover uses approximations to speed up pattern computation • To verify that the approximations are sound, an independent Monte Carlo detection simulation was built into the tool • The simulation is time driven: • Execute the search pattern with an individual dwell time resolution (10-100 msec) • Propagate the threat position with the pattern execution time • Draw random RCS values according to the threat RCS distribution • Declare detection if the return signal is strong enough (including verification) • Declare a leaker if the threat passes its last detection time undetected • Repeating this process many times results in trajectories detection statistics which can be compared to the approximate trajectories Pd’s

14. ADDITIONAL CAPABILITIES – MONTE CARLO DETECTION SIMULATION (Cont.) Monte Carlo detection statistics of a very large set of trajectories from the threat set Trajectories are ordered according to their BeamCover reported Pd Excellent agreement between BeamCover’s approximation and Monte Carlo statistics • BeamCover Pd approximation • 98% boundaries of bluecurve’s binomial distribution (5000 attempts) • Monte Carlo statistics Pd Trajectory index

15. TOOL APPLICATIONS • Optimal performance: • Comparison between sensor options • Deployment recommendations • Search pattern design • Impact of mission trade-offs

16. MISSION TRADEOFFS • The search radar provides a given engagement battlespace for a defined level of resources • In cases of load or sensor degradation it may be necessary to reduce performance of radar search • There are various approaches to reallocate the sensor search resources that will have different impact on the defense mission • Potential trade-offs include: • Timeline compromise – delay detection by t • Pd compromise • Reduced threat set (launch sites or impact sites)

17. MISSION TRADEOFFS (Cont.) In this example, Pd degradation seems more effective than timeline degradation to reduce resources

18. SUMMARY • Appropriate allocation of radar resources for search can have a critical impact on defense system performance • BeamCover is a flexible search patterning tool, designed to allow the architecture planner to make the most out of the (limited) search resources at his / her disposal • The BeamCover tool can be used to assess the search capability of a complex multi-radar defense architecture • The tool is being used both to assess how to expand Defense Architectures as well as to find algorithms for operation under degraded performance