1 / 32

Physics-based Modelling of Foliage-Camoulaged Targets

Physics-based Modelling of Foliage-Camoulaged Targets. K. Sarabandi and M. Dehmolaian. Radiation Laboratory The University of Michigan, Ann Arbor, MI 48109-2122 saraband@eecs.umich.edu. Outline:. Progress Since August 2004.

aron
Download Presentation

Physics-based Modelling of Foliage-Camoulaged Targets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Physics-based Modelling of Foliage-Camoulaged Targets K. Sarabandi and M. Dehmolaian Radiation Laboratory The University of Michigan, Ann Arbor, MI 48109-2122 saraband@eecs.umich.edu

  2. Outline: Progress Since August 2004 • The High frequency GOPOPO foliage and hard target model is enhanced by incorporating a PTD method to account for edge diffractions on the target. The model verification using scaled targets is completed. • 2. A polarization synthesis optimization method for improving signal to clutter is performed by applying a genetic algorithm for finding an optimum polarization that enhances signal to clutter ratio. • 3. The hybrid FDTD and Foliage model is completed and verified. The model can be utilized in VHF to low UHF bands (20-400 MHz) and is developed to investigate the scattering behavior of hard targets embedded inside a forest canopy.

  3. Enhanced GOPOPO model for Hard Targets • Fast GOPOPO algorithm is enhanced by including the edge effects • Edge currents are added locally using PTD Phase shh Backscatter GOPOPO+PTD MoM z 1st order GOPO Incidence angle Length of 10l along the y axis. Frequency = 2 GHz 8l

  4. Validation Using Scaled Models UoM Anechoic Chamber Transmit Antenna UoM 93-95GHz fully Polarimetric Radar Coherent-on-receive Dynamic range: 100 dB Noise equivalent RCS: -30 dBsm Scaled tank, used for radar measurement in the anechoic chamber of Radiation Laboratory

  5. Comparison of theory with measurement

  6. Target Detection Enhancement Using Multi-Polarization Channels • Target and clutter backscatter are both strong functions of polarization state of the radar transmit and receive polarization • Is there a polarization that minimizes clutter response in a statistical sense? • Is there a polarization that maximizes the foliage-embedded target response? Or target/clutter response in a statistical sense? • What are the variability of the optimal polarization on a pixel basis?

  7. Simulation Scenario: A metallic target embedded in a coniferous forest stand. Ten trees around the target are considered (tree density=0.05 tree/m2) Radar: Fully polarimetric operating at S-band. (amplitude and phase in four polarimetric channels) Simulation includes near-field interaction of foliage with target and vice versa (hybrid GOPOPO/foliage model) Polarimetric clutter response and target response (including foliage interaction) are calculated.

  8. Metallic Target Inside the Forest Target on the ground in the absence of foliage 3l Response of target inside forest Fluctuations are due to the target-foliage interactions. 3l 4l 10l Frequency = 2 GHz. 8l

  9. Cross-pol response Significant cross-pol is generated from target-foliage interaction 8l Cross-pol to co-pol ratio of about -15 dB is predicted for the target embedded in the forest.

  10. Backscatter coefficients of forest (Clutter Response) Frequency = 2 GHz Number of Realizations = 10 Density of trees = 0.05 trees/m2 Fluctuation range Max Mean value Min vertical horizontal Note: Horizontal polarization has higher RCS values. This is due to the Brewster angle effect on tree trunks.

  11. Polarization Signature Plot of target response as a function of radar polarization state Two special cases: Co-pol and Cross-pol responses Target Including foliage interaction CO-POL CROSS-POL

  12. Clutter response: generated from Avg. co-variance matrix <CROSS-POL> <Co-POL>

  13. Backscatter RCS of target Co-pol. response occurs at HH polarization. • Unfortunately clutter Co-Pol. Response also occurs at HH-polarization • Co- or Cross-pol. Configurations are not necessarily the best configuration for target detection. • Minimum clutter response occurs at c=-30 and y=50, but the target response is also weak there. • Need to search for optimal polarization • that minimizes clutter response • Or in cases target response is known a priori (e.g. through target/forest simulation) a polarization that maximizes target-to-clutter ratio

  14. Problem Statement 1- Clutter minimization Search for a set of polarizations that minimizes the maximum clutter response among all forest realizations (m). 2- Target-to-clutter maximization Search for a set of polarizations that maximizes the minimum target/clutter response among all forest realizations (m). These cost functions are highly non-linear: use a genetic algorithm search based optimization method

  15. Polarization Optimization Procedure Using Genetic Algorithm There is a one-to-one correspondence between and a point on Poincare Sphere 1- Discretization of polarization state Uniform polarization (Dc=10) produces 13-bit Gene. and require 26-bit Chromosome (226 possibilities) 2- Random generation of initial population 3- Evaluation of Chromosomes using the optimization cost function 4- Convergence? 5- Natural selection (discarding poor performing Chromosomes) 6- Mating and mutation 7- Recursion of steps 3 through 6

  16. Flowchart of Digital GA

  17. Input Data for GA Number of forest realizations = 10 Monte Carlo simulations Density of forest = 0.05 trees/m2 Pixel area = 200 m2 Frequency = 2 GHz Radar Incidence angle: • 10 Covariance matrices for forest • 10 Covariance matrices for Target embedded inside forest

  18. Convergence of GA Algorithm Convergence Achieved after 20 iterations. Seed: Initial population Note: Clutter response is computed from an area of 200 m2 (target occupies less than 1 m2 )

  19. Optimal Tilt angles Results of GA for different seeds Optimal ellipticity angles Two solutions exist Different initial seeds produce similar results as expected

  20. Optimum Polarization 1 Maximize target to clutter backscattering RCS ratio +10 dB Solution2 Solution1 Optimum polarization will provide 10 dB improvement on target/clutter ratio compared to tradition pol. configurations. T T R R

  21. Results of GA for Clutter minimization Optimal Tilt angles Optimal ellipticity angles Two solutions exist Different initial seeds produce similar results as expected

  22. Clutter Minimization Minimize clutter backscattering RCS -10 dB Solution2 Solution1 R R Optimum polarization will provide 10 dB reduction in clutter backscatter compared to tradition pol. configurations. T T

  23. Summary of Polarization Synthesis • Polarization agile radars (different polarization modality) have the ability to suppress clutter. • If important polarization signature of desired targets are known polarization synthesis can drastically enhance S/C. • Physics-based target foliage model can provide foliage-embedded target signature

  24. Hybrid FDTD/forest model • Using the coherent forest model, calculate the fields on an FDTD boundary enclosing the target. h-pol. or v-pol. 2. Using FDTD, compute the scattered fields from the target on the same grid. including all interactions

  25. Hybrid FDTD/forest model Observation point exchanging Source point • To calculate the effect of the forest on the scattered field, apply the reciprocity theorem. • So source & observation are exchanged. Note: Using this procedure, interaction between forest & target is inherently taken into account.

  26. Hybrid FDTD and Foliage Model Based on reciprocity: can be any equivalent current which can generate scattered field from target. On FDTD Box: Therefore:

  27. Flow Chart (1) (2) Get the time domain response Incident field from the forest on FDTD box Fourier Trans. (3) Solve scattering from hard target get the scattered field on the FDTD box Must be run for two fundamental incident wave polarizations and at many frequencies over the desired band FDTD : Fourier Trans. (4) Get frequency domain (5) Apply reciprocity to find target/foliage backscatter

  28. Validation of Hybrid Method (1) 3m x 3m x 3m Dihedral in free space (no foliage) VHF band (20-200 MHz) GOPOPO+PTD agree well at high frequencies Time Domain Frequency Domain Very good agreement between direct FDTD and Hybrid method

  29. Validation of Hybrid Method (2) 3m x 3m x 3m Dihedral above a ground plane = 5.6 + i 0.9 Note: An absorber layer is considered just below the FDTD box to suppress ground reflections from shadowed area.

  30. Target Simulation inside the forest target response alone No foliage shh Horizontal Polarization: Phase with foliage Number of Trees = 8 Density of forest = 0.05 Simulation area = 160 m2 VHF frequency band Vertical Polarization: No foliage Phase svv with foliage Note: Significant target foliage interaction at higher frequencies.

  31. Comparison in Time Domain Total response = Foliage + Target Horizontal Polarization: Vertical Polarization: Note: Forest distorts the target response.

  32. Summary • After 2.5 year we are now in a position to consider radar response of varieties of targets, foliage at different frequencies, polarizations, incidence angles, etc. to try multi-modal target detection. • Phenomenology of target/foliage interaction can be carried out most accurately

More Related