Presentation on theme: "PROGRESS IN SAR SHIP DETECTION AND WAKE ANALYSIS"— Presentation transcript:
1PROGRESS IN SAR SHIP DETECTION AND WAKE ANALYSIS J.K.E. TunaleyLondon Research and Development Corporation,114 Margaret Anne Drive,Ottawa, Ontario K0A 1L0This is describes Company activities in ship detection and ship wake analysis since autumn Because of the wide scope, the results will only be summarized. Details can be found on the website; these include further summaries and papers.The overall concept is that MDA relies on AIS as the principal ship information and confirmation of AIS data (location, size, speed, propulsion) can be provided by SAR images as well as databases of ship information. Wake information is expected to be relatively low grade but should be useful in AIS verification.
2OUTLINE K-distribution Ship Wakes See Web site for papers New simple asymptotic approach for large threshold valuesParameter estimation difficultiesImplications for ship detectionShip WakesStudy started at RMC, Kingston using RADARSAT-2 images, AIS, plus other information.Findings and implications for MDASee Web site for papers1.Sea clutter statistics are important for ship detection.An asymptotic calculation approach is appropriate to minimize computations and speed data delivery.Clutter statistics must be described from a limited number of pixels; this affects accuracy of parameters.Effects lead to increase in false alarms or reduction in probability of ship detection.Turbulent wake is most common wake in SAR images.Wakes have a potential for confirming (or otherwise) AIS ship identification using wake information and ship data database.
3K-Distribution Ships are bright blobs in SAR images Need statistics of clutter background for CFARK-D excellent description of radar clutterBasis is modulated complex Gaussian clutterPhysical / statistical basis incompleteModulating distribution assumed gammaModified Bessel functions of 2nd kind.Computational complexityApproximation for tail values neededShip detection in SAR images must be accomplished against a sea clutter background. Detection thresholds must be derived from the observed clutter, which is typically variable across the image. In an operational system, the time taken to detect ships must be low to avoid staleness. The processing time for a typical SAR image (RADARSAT, Envisat) should be a fraction of a minute, in other words a few seconds.Experimentally, K-distribution is usually an excellent fit to clutter data in SAR images.It can be explained as a modulation process but the distribution of the modulation is assumed to be gamma. (Birth, death, migration stochastic process; too vague.)Modified Bessel functions of the second kind take too long to calculate.We only need values out in the tail of the distribution because false alarm rate must be about 10-9 per resolution cell.This raises questions about the validity so far out in the tail.
4K-D APPROXIMATION Low PFA: interested in tail of distribution Represent pdf as an integralUse steepest descentsAccurate to better than 0.1% (PFA = 10-9)Not sensitive to statistics of modulationImplies that K-D has sound physical basisBasic code can be implemented in < 18 lines of C# or C++.Code is very small with no loops (except for gamma function, which can be found efficiently).PFA is typically very small because there are about 100,000,000 pixels in an image.Integrand replaced by a simpler one with a Gaussian shape.
5THRESHOLD COMPARISON LOOKS L = 1 L = 4 PFA Accurate Approx. 10-9 0.5 SmoothnessAccurateApprox.10-90.5214.7214.891.5991.625.047.4947.5018.79618.80050.024.248.8418.84210-695.4395.5546.4046.4325.6925.7011.26311.26715.33715.3386.128Table shows a comparison between accurate and approximate evaluations of ship detection thresholds for single look and four-look SAR images.K-distribution is characterized by a mean and a shape or order parameter, nu and the number of looks, L.It is easy to estimate the mean accurately so that we focus on nu and derive a threshold as a multiple of the mean. Nu = 0.5 represents spiky clutter, Nu = 50.0 smoother clutter.These data were calculated by integrating the pdf down from very large values until a threshold was reached where the PFA was 10-6 or 10-9.
6PARAMETER ESTIMATION Need to estimate mean and order parameter Number of looks is givenCan estimate optimal performanceUses Fisher information (Cramer Rao)Parameter variance depends on number of independent samples, NNeed to consider biasNote: Parameters need not be integerNumber of looks is given from the radar and image processing specification.It is possible to estimate optimal performance for finite number of independent samples (resolution cells or larger).Parameters need not be integers; this seems to have provided problems in the past.
7OPTIMUM MEAN INTENSITY Cramer Rao Bound #Samples N = 256L = 1L = 4Spiky clutter is at left and smooth, Rayleigh type clutter on the right.When these curves are compared to those for the mean as a simple average, they are indistinguishable on the plot. (Calculate standard deviation of the mean of N samples; the standard deviation is estimated from the theoretical distribution variance.)However, the standard deviation for N = 256 is rather high compared with the mean ( = 1). Therefore for spiky clutter, it is questionable whether N = 256 is sufficient.L = 10SpikyRayleigh
8SDs USING MOM: N = 1000 Optimum Practical Comparison of optimum and the practical “Method Of Moments” (ratio of the standard deviation of the data to the mean). MOM is quite poor for estimating nu, especially in spiky clutter at right hand side of plots. Note that when nu is large, the probability distribution is reasonably smooth and does not change very much with Nu. These plots are for N = 1000.Note that the standard deviation is for 1/Nu. This is for technical reasons.L = 1 Black; L = 4 Red; L = 10 Yellow
9PRACTICAL THRESHOLD N = 100 Ideal N = 1000 N = 10000 L = 4 Rayleigh This shows the effect of a finite number of resolution cells, N, when the specified PFA is 10-9 per resolution cell. N less than 1000 results in a large increase in PFA unless the threshold is increased to compensate. The plot is for 4-look imagery. The calculations use the K-distribution approximation together with normally distributed randomization with a variance based on MOM. There is serious bias compared to the ideal infinite N curve, even with smooth clutter..L = 4SpikyRayleigh
10CONCLUSIONS (1)K-distribution approximation will reduce computational complexity for ship detectionMethodology adds support to use of K-distributionInsensitivity to modulating distributionMean of K-distribution can be estimated as usualParameter variance may bias detection thresholds by large factors if N < 1000Very important in spiky clutterWithout correction, PFA may increase by orders of magnitudeIf corrected, probability of ship detection is reducedAdaptive pixel block size (N) is desirable in variable clutter
11SHIP WAKES Turbulent wake study with Dan Roy at RMC RADARSAT-2 imagesAISOther ship information about propulsion system (Ship owners, Internet, etc.)Analysis (60 ships) includesTwin screws/single screwLeft/right handed screwsDan Roy was a MSc student at RMC jointly supervised by Prof. Joseph Buckley and myself.Information gathered by RADARSAT2, AIS, weather stations, Internet and ship owners.Focus on explaining turbulent wakes in SAR images in terms of ship size, propulsion system, ship/radar geometry and wind speed/direction.
12RMC RESULTS Wakes not usually visible when wind speed U > 6 m/s Ship speed V is important: if U < 6 m/s && V > 5 m/s, 80% of wakes are visibleBright line on side of wake consistent with propeller flows (swirling and axial) and wind directionWakes from shallow twin screws tend to be visibleNote that velocity bunching shifts must also be taken into account.
13RSAT-2: QUEEN OF ALBERNI Queen of Alberni is part of BC Ferries fleet.Double ended ferry with a screw at each end to avoid turning at points of embarkation.Note bright line on port side of wake.The purple arrow is the wind vector.Wake offset is consistent with service speed of 19 knots.Data supplied by MDA Corporation
14Q of A Parameters Parameter Length (m) 139 Maximum Beam (m) 27.1 Mean Draft (m)5.5Maximum Draft (Prop. Tip, m)5.72Block Coefficient (estimated)0.6Number of Propellers1Number of Blades4Propeller Shaft Depth (m)3Propeller Diameter (m)5Propeller TypeCPPService Speed (knots)19Propeller 19 knots (rpm)170Maximum Power (MW)8.83Data is from the Internet and discussions with BC Ferries.
15COMBINED SWIRLING AND AXIAL WAKE Consider both linear and angular momentum in propeller wakeModify Prandtl’s approach to theoryEstimate fluid linear and angular momentum using standard engineering methodsApply to Queen of Alberni (BC Ferries)Note that shaft power came out to be 7.9 MW at service speed: compare with table value of 8.8 MW. This is very encouraging.
16Q of A Wake Diameter Combined Swirling Axial Axial and swirling eddy diffusion contributes to wake broadening. In the far wake, axial diffusion dominates. Net slope is reduced from 0.25 and to about 0.3.
17Queen of Alberni Maximum Surface Flow Speed AxialSwirlingGroup velocity of Bragg waves at C-band is about 13 cm/s. When flow speeds are comparable, the hydrodynamic wake can produce a large effect on the radar wake. Effects should be visible in calm waters when flows are as low as 2 cm/s.Note Queen of Alberni service speed 19 knots.The curves are the result of the new theory of combined axial and swirling turbulent wakes.Surface flows capable of affecting SAR image out to 10 km or more from axial component and about 1 km from transverse swirling component.
18Deep Screw Case Maximum Flow Speed AxialWhen the screws are deep, the swirling component tends to predominate at small distances astern. This is for propeller tips at a depth of a few metres.Swirling
19CONCLUSIONS (2)Surface flows in the turbulent wake can be large compared with Bragg group velocityExpect significant radar wake visibility for long distancesSwirling component dominates axial flow immediately astern and especially when screws are deepWake characteristics can be used to verify shipNote:Hydrodynamic wake width is only one factor in radar wake width. Others are flow speeds, ambient wind and waves, radar effects and geometry.