María-Pilar Jarabo-Amores

Classification of aircraft using micro-Doppler bicoherence-based features

In the work presented here we propose a novel bicoherence-based method for the classification of aerial radar targets in automatic target recognition (ATR) systems. The possibility of classifying aerial targets using the micro-Doppler contributions caused by a jet engine or the rotor of a helicopter is studied. The method is based on classification features computed in the form of bicoherence estimates, as well as cepstral coefficients extracted from the micro-Doppler contribution contained in radar returns. The performance of the classification method developed is compared with the performance of common methods using high-resolution radar range profiles (HRRPs). Correct classification probability rates are computed for three different types of aerial targets. The benefits achieved by using bicoherence-based classification features are demonstrated and discussed.

Study of Two Error Functions to Approximate the Neyman–Pearson Detector Using Supervised Learning Machines

A study of the possibility of approximating the Neyman-Pearson detector using supervised learning machines is presented. Two error functions are considered for training: the sum-of-squares error and the Minkowski error with R = 1. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition previously formulated. Some experiments about signal detection using neural networks are also presented to test the validity of the study. Theoretical and experimental results demonstrate, on one hand, that only the sum-of-squares error is suitable to approximate the Neyman-Pearson detector and, on the other hand, that the Minkowski error with R = 1 is suitable to approximate the minimum probability of error classifier.

Combining MLPs and RBFNNs to Detect Signals With Unknown Parameters

The detection of Gaussian signals with an unknown correlation coefficient rhos is considered. Solutions based on neural networks (NNs) are studied, and a strategy for designing committee machines in a composite hypothesis test is proposed. A single multilayer perceptron (MLP) has been trained with rhos uniformly varying in [0, 1]. Considering the decision boundaries for rhos = 0 and rhos = 1 and how an MLP approximates them, a detection scheme composed of two MLPs has been proposed. One of them MLP1 has been trained with rhos uniformly varying in [0, 0.5], and the other one MLP2 has been trained with rhos uniformly varying in [0.5, 1]. For making a decision, the higher output is compared to a threshold for each false-alarm probability (P FA). This strategy simplifies the task of finding a compromise solution between the computational cost and the approximation error and outperforms the single-MLP detector. When MLP1 is substituted with a radial basis function NN (RBFNN), a new combination strategy of the outputs is required. We propose separately thresholding the outputs and applying them to an or logic function. The performance of this detector is slightly better than the two-MLP one, and the computational cost is significantly reduced.

Artificial neural network-based clutter reduction systems for ship size estimation in maritime radars

The existence of clutter in maritime radars deteriorates the estimation of some physical parameters of the objects detected over the sea surface. For that reason, maritime radars should incorporate efficient clutter reduction techniques. Due to the intrinsic nonlinear dynamic of sea clutter, nonlinear signal processing is needed, what can be achieved by artificial neural networks (ANNs). In this paper, an estimation of the ship size using an ANN-based clutter reduction system followed by a fixed threshold is proposed. High clutter reduction rates are achieved using 1-dimensional (horizontal or vertical) integration modes, although inaccurate ship width estimations are achieved. These estimations are improved using a 2-dimensional (rhombus) integration mode. The proposed system is compared with a CA-CFAR system, denoting a great performance improvement and a great robustness against changes in sea clutter conditions and ship parameters, independently of the direction of movement of the ocean waves and ships.

Statistical Analysis of SAR Sea Clutter for Classification Purposes

Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.

MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

Neural Networks based CFAR techniques are proposed in an improved coherent radar detector.A coherent detector using a unique CFAR is compared to the classical bank of CFAR techniques.The filter bank output is statistically analyzed to prove Gaussian CFARs unfeasibility.The proposed neural CFAR can be applied to any clutter distribution or detection strategy.A comparative study is carried out on a simulated scenario with complex target trajectories. This paper tackles the detection of radar targets with unknown Doppler shift in presence of clutter. A Neural Network based Constant False Alarm Rate (CFAR) technique is proposed for adapting the detection threshold in an improved architecture based on the Generalized Likelihood Ratio (GLR) detector. Detection schemes based on Doppler processors (Moving Target Indicator (MTI) and Moving Target Detector (MTD)) and conventional CFAR detectors are considered as reference. In these reference solutions, interference is assumed Gaussian and white at the output of each Doppler filter, so conventional incoherent CFAR detectors are applied. The outputs of the CFAR detectors are combined using an OR operation to decide the presence of a target if, at least, one of the CFARs declares it. As a result, the P FA is higher than the desired one, as we prove. In this paper, an improved detector is presented that combines the following features: a better approximation to the Neyman-Pearson detector based on the GLR (selecting the maximum filter bank output), and a unique CFAR detector applied to the squared modulus of the maximum filter bank output. Due to the non-linear nature of the maximum function, conventional CFAR detectors are not suitable. A Neural Network CFAR solution is proposed. A general design method is presented. Results prove that the designed CFAR allows the exploitation of the better detection capabilities of the detector based on the maximum function, providing a higher probability of detection while fulfilling the probability of false alarm requirement. The proposed method can be extended to other detection strategies and radar scenarios.

Neural network based solutions for ship detection in SAR images

Ship detection is nowadays quite an important issue in tasks related to sea traffic control, fishery management and ship search and rescue. Although it has traditionally been carried out by patrol ships or aircrafts, coverage and weather conditions can become a problem. Synthetic aperture radars can surpass these coverage limitations and work under any climatological condition. Two ship detectors are proposed in this paper. The first one is a MLP-based detector that uses K-distribution parameters to characterize the sea clutter and the brightness of the pixels to detect ships. The second one is the double parameters model, DPM, proposed in the literature. While the DPM-based detector gives rise to some false alarms, leading to the need of a discrimination stage, and has some troubles when the ships are in rough water, the MLP-based detector along with the combination of both sea and ship features obtains better results in terms of detection and false alarm rates.

Detection of known targets in Weibull clutter based on Neural Networks. Robustness study against target parameters changes

The coherent detection of targets in presence of clutter and noise is considered in this study. Several clutter models are proposed in the literature, although the commonly used for sea and land clutter returns is the Weibull one. Our case of study involves that the target is known a priori, the clutter is Weibull-distributed and a white Gaussian noise is present. In this case, obtaining analytical expressions for the optimum detector is very difficult, so suboptimum solutions are taken as reference. One of this solutions is the target sequence known a priori (TSKAP) detector. This detector has several problems because it is designed for specific target and clutter parameters. So, in order to reduce these problems, a new solution is proposed, which is based in neural networks (NNs). The NNs selected are the MultiLayer Perceptrons (MLPs), which are able to learn from different environments. But, what does it happen if the radar (target or clutter) testing conditions are different of the design ones? In this case, a robustness study with respect to the target Doppler frequency is done for different radar conditions, which shows that the behavior of the proposed solution against this changes is better than the detector taken as reference, the TSKAP detector.

Radar detection with the Neyman–Pearson criterion using supervised-learning-machines trained with the cross-entropy error

The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman–Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.

First results on ground targets tracking using UHF passive radars under non line-of-sight conditions

This paper tackles the evaluation of a Kalman filter-based tracker with real data acquired by a Passive Bistatic Radar (PBR) system. The detection of slow moving targets manoeuvres in ground environment is considered. This radar scenario is characterized by shadowed areas with detection losses due to diffraction phenomena associated with trees, buildings, etc. The application of the designed tracker to real data acquired by a PBR system allows us the study of the detection capabilities to identify tracks in line-of-sight and non line-of-sight areas.