Christopher L. Brown

Signal processing techniques for landmine detection using impulse ground penetrating radar

Landmines are affecting the lives and livelihoods of millions of people around the world. A number of detection techniques, developed for use with impulse ground penetrating radar, are described, with emphasis on a Kalman filter based approach. Comparison of results from real data show that the Kalman filter algorithm provides the best detection performance, although its computational burden is also the highest.

A nonparametric approach to signal detection in impulsive interference

Impulsive interference poses a challenge to conventional detection techniques. The /spl alpha/-stable distribution, while sometimes providing a good model for impulsive interference, has proven to be no exception. Some of these difficulties have motivated the investigation and development of computationally tractable, locally suboptimum correlation, and rank correlation detectors for signals embedded in impulsive interference modeled as a symmetric /spl alpha/-stable process. The performance of some of these detectors is evaluated and compared with that of the locally optimum (LO), locally optimum rank (LOR), matched filter, and Cauchy detectors. Simulation results show that a rank-based detector is able to approach the performance of the computationally intensive LO and LOR detectors across a range of values for for /spl alpha/.

Testing for Impulsive Behavior: A Bootstrap Approach

Abstract Brown, C. L., and Zoubir, A. M., Testing for Impulsive Behavior: A Bootstrap Approach, Digital Signal Processing 11 (2001) 120–132 Increasingly, systems are being designed to account for impulsive behavior that may be present in signals, with one of the widely used statistical models used being the α stable distribution. Two techniques are presented that test for the level of impulsive behavior: testing the parameter α directly and a characteristic function (cf) based technique. The parametric bootstrap is used in both cases to estimate the distribution of the test statistics and in the setting of critical values. Simulation results show both tests maintain their level and achieve high rejection rates under alternatives.

Application of time-frequency techniques for the detection of anti-personnel landmines

In this paper we propose two methods to detect buried underground objects. Both methods are based on time-frequency analysis. The first approach uses the instantaneous frequency of the return ground penetrating radar (GPR) signals and the second approach uses a time-frequency distribution (such as the Wigner-Ville distribution or the spectrogram) of the return signals. Real data were used in the examples to validate the proposed algorithms.

Suboptimal robust estimation using rank score functions

A parameter estimation scheme based on the adaptive modelling of the score function of M-estimators is presented. The weights of basis functions are estimated from the data to match the empirical distribution. The bases utilise rank based score functions to remove dependence on scale from the basis selection process. While determination of appropriate bases for a distribution is shown to be possible, the robustness and adaptivity of the scheme means good results may be achieved regardless.

Landmine detection using single sensor metal detectors

Historically, metal detectors have been essential tools for demining. However they have been unable to keep pace with developments that made landmines more difficult to find. Here, techniques for the detection of buried objects using a metal detector are presented, evaluated and compared. The findings highlight a number of deficiencies, as well as a number of strengths, in the proposed detectors. Of particular interest are the parameters found using Pronys method, as well as the difference operator, reverse arrangements test and the median filter. Suggestions are made for the improvement of a number of detectors.

Score functions for locally subptimum and locally suboptimum rank detection in alpha-stable interference

Approximations to the locally optimum and locally optimum rank score functions for the detection of a known signal in additive symmetric /spl alpha/ stable interference have been shown to introduce only slight performance loss. Here, the location of the apices of the nonlinearities is shown to follow an approximate linear relationship with the characteristic exponent, /spl alpha/, of the interference. The distribution of the corresponding test statistics is also found to be approximately Gaussian. These findings make implementation of the detectors more feasible and remove some expensive computational burden.

Polynomial phase signal based detection of buried landmines using ground penetrating radar

Put simply, the global landmine problem is massive. Ground penetrating radar (GPR) is just one engineering solution currently being investigated. A polynomial amplitude-polynomial phase model is fitted to GPR returns. It is observed that the second order phase coefficient shows deviations from background-only levels when a buried target is present. A bootstrap-based detection scheme is proposed that tests for this change. The technique is applied to real GPR data, with encouraging results.

Model selection for adaptive robust parameter estimation and its impact on multiuser detection

Robust parameter estimation in impulsive noise environments has become an important issue in wireless communications. In previous work, an adaptive robust estimator was developed which modelled the noise score function as a weighted sum of basis functions where the weights best fitted the empirical distribution. Here, this adaptive robust estimator is extended by using model selection to find a parsimonious set of basis functions to model the unknown noise distribution thereby improving small sample performance. It was found that the best model for small sample sizes is a single basis. Finally, we apply this procedure to robust multiuser detection in impulsive noise channels.

Locally optimum and rank-based known signal detection in correlated alpha-stable interference

The problem of detecting known signals in the presence of correlated interference with symmetric alpha-stable excitation is considered. An estimation procedure for the signal strength is devised, based on the minimum dispersion criterion, then blind channel identification is performed. Several detection schemes are presented based on locally optimum (LO) and locally optimum rank (LOR) procedures, as well as some more computationally efficient suboptimal tests. The approximate distributions of the corresponding test statistics are derived. Simulation results indicate the levels of significance are maintained and all detectors achieve similar detection rates. The conclusion is drawn that the LOR and the suboptimal tests are able to achieve near locally optimal performance but with a far lighter computational burden.