Fuzing Validation. RAeS WS&T Conference November 2012 Jason Cowell. Thales Proprietary LAND DEFENCE

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1 Fuzing Validation RAeS WS&T Conference November 2012 Jason Cowell

2 2 / Content Introduction & Background Proximity Fuze Development Challenges for proximity fuzes Advancement in signal process capability Principles of operation and applications Cloud discrimination algorithms Cloud data gathering Algorithm development Sea Clutter discrimination Algorithm development Validation through trials Summary Acknowledgement: The work described in this presentation has been sponsored by the UK MoD

3 3 / Introduction Previous LIDAR based fuzes Proven and reliable sensor technology In-service with several UK missiles Existing products employ Bespoke laser diode and detector components Special to purpose ground and polished glass optical components Optical alignment mechanisms and associated bonding processes Machined metal housings and structural components ASICs (Application Specific Integrated Circuits) Performance constrained by simple detection & trigger processing logic Simple received signal threshold crossing & event counting Limited clutter discrimination capability Advanced Short Range Air to Air Missile Prox Rapier Ground to Air Missile Prox

4 4 / Modular Prox Strategy Modular Strategy In the last 5 years TME has developed a new approach to Prox fuze design Common Signal Processor module compatible with both future laser and radar sensor technologies Associated laser and radar modules designed to be compatible with Common Processor Modular family of Prox fuze sensor products to meet a wide range of future applications Improved flexibility of algorithmic approach to deal with a range of environments Dual Mode Options Laser Transceiver Modules + Common Signal Processor + Radar RF Transceiver Modules Laser Sensor Product Family Radar Sensor Product Family

5 5 / Common Signal Processor Fully developed item (4 th iteration) Low cost components Dual Channel Analogue to Digital converter Field Programmable Gate Array Digital Signal Processor Read Only Memory Timing control & address lines Multiplexed interface / control of multiple analogue transceiver modules Powerful platform for algorithms Optimised triggering effectiveness Enhanced clutter discrimination Real time analysis of time history of received signal waveforms Exploit differences in texture between background and target φ<2½ inches φ60mm Weapon External interface Serial data link, Trigger to SAU FPGA Data Buffering, Timing & Control Dual Core DSP e.g. AD Black Fin x5 Timing control lines x2 Address Lines 2x10 Bit ADC Two Channel 100Ms/s Ch2 Memory Ch1

6 6 / LIDAR Prox Principle of Operation Concept of Operation Multiple fan beam LIDAR sensor Precise hollow cone overall envelope Pulsed laser emissions in each beam Detection processing in each beam Nose on view Received pulse digitisation & integration Pulse time of flight down beam range measurements in each beam Side view Clutter discrimination processing Sea clutter, clouds, smokes, etc Based on time history of pulse returns

7 7 / Demonstration Unit with Visible Laser Diodes Red laser diodes fitted to illustrate beam geometry Near IR diodes used in real sensor

8 8 / Performance Validation Strategy Performance validation strategy To avoid expensive weapon level trials the fuze performance predictions are based on modelling results Fuze mathematical model is validated through cost effective trials Sensor characterisation Signatures of targets Signatures of background Signatures of clutter Algorithm development utilises the trials data to develop and then prove algorithm (detection & rejection of false alarms) performance Model vast numbers of engagement scenarios in Monte-Carlo assessment Range of missile engagement geometries Range of target geometries Range of environmental conditions Range of clutter or background condition

9 9 / Effects of cloud on Air target Fuze Cloud impact on Prox fuze performance Historically radar fuzes have been used which are broadly unaffected by clouds Simple detection processing for laser fuzes can result in false alarms Improvements in signal processing techniques provide opportunity for enhanced clutter discrimination for laser Prox fuzes Attenuation of signal not major issue over short ranges Aerosols provide backscatter of laser Cloud edges provide change in signal level that could cause false alarm When entering or leaving cloud (at interface) Robust algorithmic approach required for laser fuzes that reject signals from cloud to avoid false alarms

10 10 / Cloud Clutter Data Gathering Experimental 4 Fan beam Laser Module Prototype Laser Prox module Integrated within sealed enclosure Laptop data logging of reflected pulses Secured against open map window Uninterrupted view of cloud Data recording in dense cloud Cumulus, Cumulonimbus In cloud, entering cloud, exiting cloud Pulse rate scaled for aircraft velocity

11 11 / Cloud Backscatter Pulse Signatures Gathered Sector 1 Sector 4 Amplitude Amplitude Digitised Pulse Waveform Time during flight (s) Digitised Pulse Waveform Time during flight (s) Sector 2 Sector 3 Amplitude Amplitude Digitised Pulse Waveform Time during flight (s) Time during flight (s) Digitised Pulse Waveform

12 12 / Cloud Clutter Discrimination Algorithms Algorithms can analyse backscattered pulse waveform history Determination of cloud presence and point of entry/exit Exploitation of received signal amplitude rate of change in multiple sectors Valid targets not falsely detected as cloud Cloud backscatter suppression techniques Slowly varying cloud backscatter waveforms can effectively be subtracted Residual signals can still occur at cloud edges Adaptive threshold techniques to fully suppress cloud detections Green Raw Pulse Amplitude Red Amplitude Post Suppression Residual post suppression spike Effective cloud clutter discrimination algorithms demonstrated

13 13 / Small targets in sea clutter Threat proximity to sea surface a challenge for the fuze Sea skimming missiles close to sea clutter Fast Inshore Attack Craft (FIACs) embedded in sea clutter Clutter reflections difficult to differentiate from target Can be similar range and amplitude Analysis of fuze performance requires representative models of sensor interaction with the sea surface Sea Skimming Missile Boston Whaler with rocket launcher

14 14 / Laser Sensor Interaction with the Sea Operating at near IR wavelength (λ~0.9 ~0.9µm) Imaginary component of refractivity (k) very small Bulk absorption high hence volume backscatter can be ignored Real component of refractivity (n) ~1.33 can be used to estimate surface reflectivity (ρ) using Fresnel Only incident angles close to normal are of interest Small sensor bistatic angle Fresnel equations simplify Reflectivity ~2% Lidar with low bistatic angle ( ) ( + 1) n 1 2 ρ = n 0.02 Sea surface

15 15 / Laser Sensor Interaction with the Sea Active IR (laser) sensor response to sea intermittent Sea surface behaves like a rippled mirror with a 2% reflectivity Strong reflection if surface elements intersect beam near normal Very low response if illuminated surface not close to normal Response depends upon complex geometry of beam and rippled shape of sea surface White caps can present a diffusely scattered signature Detected over a broad range of illumination angles Sea detection unreliable Sea detection unreliable But detection of white caps likely Sea detection may be reliable enough to track But detection of white caps likely

16 16 / Modelling the Sea Surface Sea surface modelled as an array of small 2% reflectors Contiguous surface comprising non planar facets 5mm x 5mm (or smaller) Arranged to represent 3D geometry of sea surface Model shares origins with existing radar TDD interaction model Smaller facets due to much shorter wavelength (~1µm versus ~10cm) 64bit PC with large memory capacity used to run analyses (slowly) Fuze sensor interaction model Contiguous surface comprising non planar facets Multiple fan beam geometry modelled Defined engagement trajectories Intersection of beams with 3D sea model Pulse by pulse response modelled Summation of reflected pulse components from multiple facets computed

17 17 / Modelling the Sea Surface Model uses wave spectrum proposed by Elfouhaily Both gravity & surface capillary waves modelled Capillary waves (e.g. λ<25mm) significant at laser wavelengths Parameters adjusted to vary sea conditions Fetch Wind speed & Direction Resolution (e.g. 5mm) Patch Size Wide variety of sea conditions modelled Case shown a 80m by 80m patch, 12m/s wind, 500km fetch

18 18 / Modelling Sensor Response to the Sea Sea surface modelled as a regular grid of heights Height at each vertex derived using the Elfouhaily spectrum Characteristics of each element calculated from adjacent vertices Normal vector of each element Radii of curvature in two orthogonal axes Intersection of beams with grid Shot lines calculated to each element Occurrences of surface normals found Incremental contributions to pulse responses determined from; Sensor parameters (e.g. power, etc) Element radii of curvature Repeated at Pulse Repetition Rate

19 19 / Model Validation Sea Data Gathering Initial Pencil Beam Laser Sensor Trials Pulsed laser sensor Narrow beam width <1 Sensitivity calibrated Mounted on bows of vessel Beam viewing sea surface ahead of wake Adjustable pitch & roll angles Adjustable height Vessel speed ~13 knots Wind speed/bearing recorded Threshold crossings recorded Fair correlation with model Provided initial validation

20 20 / Model Validation Sea Data Gathering Multiple Fan Beam Laser Sensor Trials Experimental form of future TDD Four 30 contiguous fan beams Partial azimuth coverage (only downward beams see reflections) Received pulse waveforms digitised Data recorded for various sensor orientations and sea conditions Example of Sector 3 pulse responses Pulse count (x10 4 ) Time (ns)

21 21 / Model Applications Anti FIAC Algorithms FIAC targets modelled 3D facet models Diffuse Lambertian reflectors Embedded in sea clutter models Various dive angles modelled Combined response to target and clutter modelled Algorithm development Sea clutter rejection Reliable target detection Initial algorithms constructed and tested Initial results encouraging Validation in progress

22 22 / Sea Data Gathering Trial Sensor deployed on boom to one side of vessel Rib target travelling at speed under / to one side of sensor Provides representative wake data Data to be used for validating models and developing algorithms 30 knots 15 knots

23 23 / Recent AFIAC Sea Data Gathering Trial

24 24 / Received Pulse Signatures Gathered Sector 1 Amplitude Amplitude Digitised Pulse Waveform Digitised Pulse Waveform 1 2 Wake Target Wake Target 3 4 Time during flight (s) Time during flight (s) Sporadic Sea reflections Amplitude Amplitude Digitised Pulse Waveform Digitised Pulse Waveform Time during flight (s) Time during flight (s)

25 25 / Laser TDD FIAC Detection Algorithms Sensor (and camera) rolled so that sector 3 looks down Algorithms can analyse received pulse waveforms Valid targets consistently detected in multiple fan beams Simultaneous sea detections very rarely occur in multiple sectors Reflected power variability Sea reflections sporadic Pulse amplitude from sea varies significantly from pulse to pulse Target signatures more consistent Range measurements Available from each TDD sector Range step onto target evident if the sea is being tracked beforehand Valid targets exhibit greater range variability and rate than the sea Effective sea clutter discrimination algorithms demonstrated

26 26 / Model Applications Anti FIAC Algorithms Example Model Output Case of Horizontal Trajectory Sea Surface Only Target Present Sector 1 - Red Sector 2 Green Sector 3 Cyan Sector 4 Magenta First target facet enters beam Target detection

27 27 / Real-time algorithm Exploits difference in reflectivity characteristics of undisturbed sea, and target / wake Two parallel processing paths Tracking of sea surface Detection of bumps Range gradient estimation Detection of sudden changes in range, which may correspond with a target profile Comparison with baseline fuze modes to determine possible benefit Target Range Calculate range gradient Detection Criteria For each Integration Dive Indication Dive Estimate Missile characteristics Missile speed model wrto time Data from the CAN (if available) Looking for sudden bump in the sea surface Sea Tracking Detection Criteria Wake Target Detected

28 28 / Summary A fuze sensor model for the response of a multiple fan beam laser fuze has been developed along with sea surface and cloud discrimination algorithms Cost effective data gathering trials have provided the evidence to validate these models Received signal levels estimated by the model compare favourably with those of the trials data The predicted variability of the signal returns from the sea appears to be confirmed by the trials Facility to embed targets in scene e.g. FIACs and sea skimming missiles Supports the development of a Laser Fuze sensor for Anti FIAC and anti Sea Skimmer missile applications Research has demonstrated the feasibility of enhanced AFIAC fuzing Smaller FIACs, embedded in high sea clutter, over a broader miss distribution Current phase is to embed algorithms into hardware for trials and then into production hardware (March 2013)

29 29 / Any Questions?

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