The SIMCA algorithm for processing Ground Penetrating Radar data and its use in landmine detection

The main challenge of ground penetrating radar (GPR) based land mine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. But because the diameter of an AP mine can be as low as 2cm and many soils have very high attenuations at frequencies above 3GHz, the accurate detection of landmines is accomplished using advanced algorithms. Using image reconstruction and by carrying out the system level analysis of the issues involved with recognition of landmines allows the landmine detection problem to be solved. The SIMCA (’SIMulated Correlation Algorithm’) is a novel and accurate landmine detection tool that carries out correlation between a simulated GPR trace and a clutter1 removed original GPR trace. This correlation is performed using the MATLAB R processing environment. The authors tried using convolution and correlation. But in this paper the correlated results are presented because they produced better results. Validation of the results from the algorithm was done by an expert GPR user and 4 other general users who predict the location of landmines. These predicted results are compared with the ground truth data

A 2D processing algorithm for detecting landmines using Ground Penetrating Radar data

Ground Penetrating Radar(GPR) is one of a number of technologies that have been used to improve landmine detection efficiency. The clutter environment within the first few cm of the soil where landmines are buried, exhibits strong reflections with highly non-stationary statistics. An antipersonnel mine(AP) can have a diameter as low as 2cm whereas many soils have very high attenuation frequencies above 3GHZ. The landmine detection problem can be solved by carrying out system level analysis of the issues involved to synthesise an image which people can readily understand. The SIMCA (’SIMulated Correlation Algorithm’) is a technique that carries out correlation between the actual GPR trace that is recorded at the field and the ideal trace which is obtained by carrying out GPR simulation. The SIMCA algorithm firstly calculates by forward modelling a synthetic point spread function of the GPR by using the design parameters of the radar and soil properties to carry out radar simulation. This allows the derivation of the correlation kernel. The SIMCA algorithm then filters these unwanted components or clutter from the signal to enhance landmine detection. The clutter removed GPR B scan is then correlated with the kernel using the Pearson correlation coefficient. This results in a image which emphasises the target features and allows the detection of the target by looking at the brightest spots. Raising of the image to an odd power >2 enhances the target/background separation. To validate the algorithm, the length of the target in some cases and the diameter of the target in other cases, along with the burial depth obtained by the SIMCA system are compared with the actual values used during the experiments for the burial depth and those of the dimensions of the actual target. Because, due to the security intelligence involved with landmine detection and most authors work in collaboration with the national government military programs, a database of landmine signatures is not existant and the authors are also not able to publish fully their algorithms. As a result, in this study we have compared some of the cleaned images from other studies with the images obtained by our method, and I am sure the reader would agree that our algorithm produces a much clearer interpretable image

The SIMCA algorithm for processing Ground Penetrating Radar data and its use in locating foundations in demolished buildings

Abstract—The main challenge of ground penetrating radar GPR) based foundation detection is to have an accurate image analysis method. In order to solve the detection problem a system level analysis of the issues involved with the recognition of foundations using image reconstruction is required. The SIMCA (’SIMulated Correlation Algorithm’) is a technique based on an area correlation between the trace that would be returned by an ideal point reflector in the soil conditions at the site and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the design parameters of the radar and soil properties. Then SIMCA takes the raw data as the radar is scanned over the ground and in real-time uses a clutter removal technique to remove various clutter such as cross talk, initial ground reflection and antenna ringing. The trace which would be returned by a target under these conditions is then used to form a correlation kernel. The GPR b-scan is then correlated with the kernel using the Pearson correlation coefficient, resulting in a correlated image which is brightest at points most similar to the canonical target. This image is then raised to an odd power >2 to enhance the target/background separation. To validate and compare the algorithm, photographs of the building before it was demolished along with processed data using the REFLEXW package were used. The results produced by the SIMCA algorithm were very promising and were able to locate some features that the REFLEXW package were not able to identify

A 3D Reconstruction Algorithm for the Location of Foundations in Demolished Buildings

The location of foundations in a demolished building can be accomplished by undertaking a Ground Penetrating Radar (GPR) survey and then to use the GPR data to generate 3D isosurfaces of what was beneath the soil surface using image reconstruction. The SIMCA ('SIMulated Correlation Algorithm') algorithm is a technique based on a comparison between the trace that would be returned by an ideal point reflector in the soil conditions at the site and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the design parameters of the radar and the soil properties. The trace which would be returned by a target under these conditions is then used to form a kernel. Then SIMCA takes the raw data as the radar is scanned over the ground and removes clutter using a clutter removal technique. The system correlates the kernel with the data by carrying out volume correlation and produces 3D images of the surface of subterranean objects detected. The 3D isosurfaces are generated using MATLAB software. The validation of the algorithm has been accomplished by comparing the 3D isosurfaces produced by the SIMCA algorithm, Scheers algorithm and REFLEXW commercial software. Then the depth and the position in the x and y directions as obtained using MATLAB software for each of the cases are compared with the corresponding values approximately obtained from original Architect's drawings of the buildings

The SIMCA algorithm for processing ground penetrating radar data and its practical applications

The main objective of this thesis is to present a new image processing technique to improve the detectability of buried objects such as landmines using Ground Penetrating Radar (GPR). The main challenge of GPR based landmine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. An Antipersonnel mine (APM) can have a diameter as little as 2cm, whereas many soils have very high attenuation at frequencies above 450 MHz. In order to solve the detection problem, a system level analysis of the issues involved with the recognition of landmines using image reconstruction is required. The thesis illustrates the development of a novel technique called the SIMCA (“SIMulated Correlation Algorithm”) based on area or volume correlation between the trace that would be returned by an ideal point reflector in the soil conditions at the site (obtained using the realistic simulation of Maxwell’s equations) and the actual trace. During an initialization phase, SIMCA carries out radar simulation using the system parameters of the radar and the soil properties. Then SIMCA takes the raw data as the radar is scanned over the ground and uses a clutter removal technique to remove various unwanted signals of clutter such as cross talk, initial ground reflection and antenna ringing. The trace which would be returned by a target under these conditions is then used to form a correlation kernel using a GPR simulator. The 2D GPR scan (B scan), formed by abutting successive time-amplitude plots taken from different spatial positions as column vectors,is then correlated with the kernel using the Pearson correlation coefficient resulting in a correlated image which is brightest at points most similar to the canonical target. This image is then raised to an odd power >2 to enhance the target/background separation. The first part of the thesis presents a 2-dimensional technique using the B scans which have been produced as a result of correlating the clutter removed radargram (’B scan’) with the kernel produced from the simulation. In order to validate the SIMCA 2D algorithm, qualitative evidence was used where comparison was made between the B scans produced by the SIMCA algorithm with B scans from some other techniques which are the best alternative systems reported in the open literature. It was found from this that the SIMCA algorithm clearly produces clearer B scans in comparison to the other techniques. Next quantitative evidence was used to validate the SIMCA algorithm and demonstrate that it produced clear images. Two methods are used to obtain this quantitative evidence. In the first method an expert GPR user and 4 other general users are used to predict the location of landmines from the correlated B scans and validate the SIMCA 2D algorithm. Here human users are asked to indicate the location of targets from a printed sheet of paper which shows the correlated B scans produced by the SIMCA algorithm after some training, bearing in mind that it is a blind test. For the second quantitative evidence method, the AMIRA software is used to obtain values of the burial depth and position of the target in the x direction and hence validate the SIMCA 2D algorithm. Then the absolute error values for the burial depth along with the absolute error values for the position in the x direction obtained from the SIMCA algorithm and the Scheers et al’s algorithm when compared to the corresponding ground truth values were calculated. Two-dimensional techniques that use B scans do not give accurate information on the shape and dimensions of the buried target, in comparison to 3D techniques that use 3D data (’C scans’). As a result the next part of the thesis presents a 3-dimensional technique. The equivalent 3D kernel is formed by rotating the 2D kernel produced by the simulation along the polar co-ordinates, whilst the 3D data is the clutter removed C scan. Then volume correlation is performed between the intersecting parts of the kernel and the data. This data is used to create iso-surfaces of the slices raised to an odd power > 2. To validate the algorithm an objective validation process which compares the actual target volume to that produced by the re-construction process is used. The SIMCA 3D technique and the Scheers et al’s (the best alternative system reported in the open literature) technique are used to image a variety of landmines using GPR scans. The types of mines included plastic, wooden and glass ones. In all cases clear images were obtained with SIMCA. In contrast Scheers’ algorithm, the present state-of-the-art, failed to provide clear images of non metallic landmines. For this thesis, the above algorithms have been tested for landmine data and for locating foundations in demolished buildings and to validate and demonstrate that the SIMCA algorithms are better than existing technologies such as the Scheers et al’s method and the REFLEXW commercial software

Landmine detection technologies to face the demining problem in antioquia

This paper presents a review of existing landmine detection techniques. The review is made with an analysis of the strengths and weaknesses of each technique in relation to the landmine detection problem in Antioquia, which ranks first in Colombia by the number of victims from landmines. According to the uniqueness of landmines and terrains in Antioquia, this paper suggests some research topics that may help in the demining task for this affected department