Maria P. Jarabo-Amores

Detection of Ships in Marine Environments by Square Integration Mode and Multilayer Perceptrons

A novel method for detecting ships in marine environments is presented in this paper. For this purpose, the information contained in the marine images obtained by a measuring and monitoring marine system is used. The ship detection is done by multilayer perceptrons (MLPs). In the first approach, the MLP processes the information extracted from the images using horizontal or vertical integration modes. However, if a suitable combination of these integration modes is done, better detection performances are achieved. Therefore, the use of an improved integration mode is proposed, which is based on a square shape. These modes are also used in a commonly used detector, the cell averaging constant false alarm rate (CA-CFAR) detector, which is taken as reference in our experiments. The comparison of the performances of both detectors shows how the MLP-based detector outperforms the CA-CFAR detector in all the cases under study. This comparison is based on objective (probabilities of false alarm and detection) and subjective estimations of their performances. The MLP-based detector also presents another advantage, particularly when the square integration mode is considered: high-performance robustness against changes in the marine environmental conditions.

Coherent Detection of Swerling 0 Targets in Sea-Ice Weibull-Distributed Clutter Using Neural Networks

The detection of Swerling 0 targets in movement in sea-ice Weibull-distributed clutter by neural networks (NNs) is presented in this paper. Synthetic data generated for typical sea-ice Weibull parameters reported in the literature are used. Due to the capability of NNs for learning the statistical properties of the clutter and target signals during a supervised training, high clutter reduction rates are achieved, reverting on high detection performances. The proposed NN-based detector is compared with a reference detector proposed in the literature that approximates the Neyman-Pearson (NP) detector. The results presented in the paper allow empirically demonstrating how the NN-based detector outperforms the detector taken as reference in all the cases under study. It is achieved not only in performance but also in robustness with respect to changes in sea-ice Weibull-distributed clutter conditions. Moreover, the computational cost of the NN-based detector is very low, involving high signal processing speed.

NN-Based detector for known targets in coherent weibull clutter

Radar detection of targets in clutter and noise is an usual problem presented in radar systems. Several schemes based on statistical signal processing are proposed as detectors. In some cases, the Neural Networks (NNs) are applied to this problem. In this article, a radar detector based in a class of NN, the MultiLayer Perceptron (MLP), is proposed. This MLP can be trained in a supervised way to minimize the Mean Square Error (MSE) criterion. Moreover, it is demonstrated that the MLP trained in that way approximates the Neyman-Pearson detector. The NN-based detector proposed is compared with a Target Sequence Known A Priori (TSKAP) detector. The last detector is only took as reference because it is not realizable due to it is necessary to know when the target exists and its magnitude and shape. The results show how the proposed detector improves the performance of the TSKAP one for different conditions of the target measured with the Signal-to-Noise Ratio (SNR) and the skewness or shape parameter (a) of the Weibull-distributed clutter. Finally, several figures show which is the improvement of the NN-based detector.

Low complexity MLP-based radar detector: influence of the training algorithm and the MLP size

MultiLayer Perceptrons (MLPs) trained in a supervised way to minimize the Mean Square Error are able to approximate the Neyman-Pearson detector. The known target detection in a Weibull-distributed clutter and white Gaussian noise is considered. Because the difficulty to obtain analytical expressions for the optimum detector under this environment, a suboptimum detector like the Target Sequence Known A Priori (TSKAP) detector is taken as reference. The results show a MLP-based detector dependency with the training algorithm for low MLP sizes, being the Levenberg-Marquardt algorithm better than the Back-Propagation one. On the other hand, this dependency does not exist for high MLP sizes. Also, this detector is sensitive to the MLP size, but for sizes greater than 20 hidden neurons, very low improvement is achieved. So, the MLP-based detector is better than the TSKAP one, even for very low complexity (6 inputs, 5 hidden neurons and 1 output) MLPs.

Approximating the Neyman-Pearson detector with 2C-SVMs. Application to radar detection

This paper presents a study about the possibility of implementing approximations to the Neyman-Pearson detector with C-Support Vector Machines and 2C-Support Vector Machines. It is based on obtaining the functions these learning machines approximate to after training to minimize the empirical risk, and on the possible implementation of the Neyman-Pearson detector with these approximated functions. The function approximated by a C-Support Vector Machine after perfect training is a binary function, with only two possible outputs. When the output of the C-Support Vector Machine is compared to a threshold, whose value is the intermediate between the possible outputs, an implementation of the Maximum-A-Posteriori classifier is obtained. On the other hand, the function approximated by a 2C-Support Vector Machine after perfect training is also a binary function, but this machine implements the Neyman-Pearson detector for a fixed probability of false alarm and probability of detection pair, that can be selected with the parameter γ which controls the costs of the error function. Some experiments about radar detection have been carried out, in order to confirm the theoretical results. The results of these experiments allow us to confirm that the 2C-Support Vector Machine can implement very good approximations to the Neyman-Pearson detector. HighlightsA study about approximations to the Neyman-Pearson detector with SVMs is presented.The functions approximated by C-SVMs and 2C-SVMs after training are obtained.2C-SVMs can be used to approximate the optimum Neyman-Pearson detector.PFAcontrol is achieved varying the ź parameter of 2C-SVM and prior probabilities.Results with synthetic and real radar data are presented to validate the study.

Performance analysis of MLP-based radar detectors in weibull-distributed clutter with respect to target doppler frequency

In this paper, a Multilayer Perceptron (MLP) is proposed as a radar detector of known targets in Weibull-distributed clutter. The MLP is trained in a supervised way using the Levenberg-Marquardt back-propagation algorithm to minimize the Mean Square Error, which is able to approximate the Neyman-Pearson detector. Due to the impossibility to find analytical expressions of the optimum detector for this kind of clutter, a suboptimum detector is taken as reference, the Target Sequence Known A Priori (TSKAP) detector. Several sizes of MLP are considered, where even MLPs with very low sizes are able to outperform the TSKAP detector. On the other hand, a sensitivity study with respect to target parameters, as its doppler frequency, is made for different clutter conditions. This study reveals that both detectors work better for high values of target doppler frequency and one-lag correlation coefficient of the clutter. But the most important conclusion is that, for all the cases of the study, the MLP-based detector outperforms the TSKAP one. Moreover, the performance improvement achieved by the MLP-based detector is higher for lower probabilities of false alarm than for higher ones.

MLP-Based Detection of Targets in Clutter: Robustness with Respect to the Shape Parameter of Weibull-Disitributed Clutter

Obtaining analytical expressions for coherent detection of known signals in Weibul-distributed clutter and white Gaussian noise has been a hard task since the last decades. In fact, nowadays, these expressions have not been found yet. This problem lead us to use suboptimum solutions to solve this problem. Optimum approximations can be done by using Multilayer Perceptrons (MLPs) trained in a supervised way to minimize the mean square error. So, MLP-based detectors are constructed and compared with one of the suboptimum detectors commonly used to solve the detection problem under study. First, a study of the dimensionality of the MLP is done for typical values of the target and clutter conditions. And finally, a deep study is done according to the variations of the most important parameters of the target and clutter signals. The last study let us to be conscious about the importance of the selection of the parameters to design both detectors. Moreover, the difference of performances between each other and the superiority of the MLP-based detector against the suboptimum solution is emphasized.