Alexandre Jouan

Speckle filtering of SAR images: a comparative study between complex-wavelet-based and standard filters

We present a comparative study between a complex Wavelet Coefficient Shrinkage (WCS) filter and several standard speckle filters that are widely used in the radar imaging community. The WCS filter is based on the use of Symmetric Daubechies wavelets which share the same properties as the real Daubechies wavelets but with an additional symmetry property. The filtering operation is an elliptical soft- thresholding procedure with respect to the principal axes of the 2D complex wavelet coefficient distributions. Both qualitative and quantitative results (signal to mean square error ratio, equivalent number of looks, edgemap figure of merit) are reported. Tests have been performed using simulated speckle noise as well as real radar images. It is found that the WCS filter performs equally well as the standard filters for low-level noise and slightly outperforms them for higher-level noise.

IMM-JVC and IMM-JPDA for closely maneuvering targets

Efficient and reliable multiple target tracking methods require the use of sophisticated data association as well as positional estimation algorithms. Two different approaches can be employed in the development of an assignment strategy for data association. This paper presents a comparative study of two assignment alternatives, namely the JVC (unique association of a measurement to an existing track) and JPDA (nonunique association of a measurement to an existing track) algorithms. The above assignment strategies are then both combined with an interacting multiple model (IMM) positional estimator, and the respective tracking performance of the IMM-JVC and IMM-JPDA algorithms is evaluated using two critical scenarios involving a pair of closely maneuvering targets.. An analysis of the simulation results is carried out, permitting for a comparison of the JVC and JPDA association algorithms, which ultimately demonstrates the superiority of the IMM-JVC algorithm.

Fusion of imaging and nonimaging sensor information for airborne surveillance

This paper presents results from an Adaptable Data Fusion Testbed (ADFT) which has been constructed to analyze simulated or real data with the help of modular algorithms for each of the main fusion functions and image interpretation algorithms. The result obtained from data fusion of information coming from an imaging Synthetic Aperture Radar (SAR) and non-imaging sensors (ESM, IFF, 2-D radar) on-board an airborne maritime surveillance platform are presented for two typical scenarios of Maritime Air Area Operations and Direct Fleet Support. An extensive set of realistic databases has been created that contains over 140 platforms, carrying over 170 emitters and representing targets from 24 countries. A truncated Dempster-Shafer evidential reasoning scheme is used that proves robust under countermeasures and deals efficiently with uncertain, incomplete or poor quality information. The evidential reasoning scheme can yield both single ID with an associated confidence level and more generic propositions of interest to the Commanding Officer. For nearly electromagnetically silent platforms, the Spot Adaptive mode of the SAR, which is appropriate for naval targets, it is shown to be invaluable in providing long range features that are treated by a 4-step classifier to yield ship category, type and class. Our approach of reasoning over attributes provided by the imagery will alloy the ADFT to process in the next phase (currently under way) both FLIR imagery and SAR imagery in different modes (RDP for naval targets, Strip Map and Spotlight Non-Adaptive for land targets).

Comparison of various schema of filter adaptivity for the tracking of maneuvering targets

Tracking maneuvering targets is a complex problem which has generated a great deal of effort over the past several years. It has now been well established that in terms of tracking accuracy, the Interacting Multiple Model (IMM) algorithm, where state estimates are mixed, performs significantly better for maneuvering targets than other types of filters. However, the complexity of the IMM algorithm can prohibit its use in these applications of which similar algorithms cannot provide the necessary accuracy and which can ont afford the computational load of IMM algorithm. This paper presents the evaluation of the tracking accuracy of a multiple model track filter using three different constant-velocity models running in parallel and a maneuver detector. The output estimate is defined by selecting the model whose likelihood function is lower than a target maneuver threshold.

Data fusion applications for military and civilian purposes developed on DND/LM Canada decision support testbed

Since 1991, the research and development (R&D) group at Lockheed Martin Canada (LM Canada) has been developing and demonstrating technologies which will provide observe-orient-decide-act (OODA) decision making capabilities/tools in naval and airborne command and control (C2) for application on Canadian Patrol Frigates (CPF) and Canadas CP-140 (Aurora) fixed wing aircraft. Over the last three years LM Canada has also established a generic expert system infrastructure and has demonstrated that it is suitable for integrating these decision making technologies into real-time command and control systems (CCSs). This infrastructure has proven to be very useful not only in the defence applications like CPF and Aurora, but also in LM Canada diversification efforts, specifically in intelligent transportation systems (ITS) and remote sensing. The paper describes the infrastructure, the generic testbed for decision support and the data fusion applications that have been implemented and are under development within this testbed for military and civilian purposes.