Assessment of RCS-specific SNR and Loglikelihood Function in Detecting Low-observable Targets and Drones Illuminated by a Low Probability of Intercept Radar Operating in Littoral Regions

The objective of this study is to deduce signal-to-noise ratio (SNR) based loglikelihood function involved in detecting low-observable targets (LoTs) including drones Illuminated by a low probability of intercept (LPI) radar operating in littoral regions. Detecting obscure targets and drones and tracking them in near-shore ambient require ascertaining signal-related track-scores determined as a function of radar cross section (RCS) of the target. The stochastic aspects of the RCS depend on non-kinetic features of radar echoes due to target-specific (geometry and material) characteristics; as well as, the associated radar signals signify randomly-implied, kinetic signatures inasmuch as, the spatial aspects of the targets fluctuate significantly as a result of random aspect-angle variations caused by self-maneuvering and/or by remote manipulations (as in drones). Hence, the resulting mean RCS value would decide the SNR and loglikelihood ratio (LR) of radar signals gathered from the echoes and relevant track-scores decide the performance capabilities of the radar. A specific study proposed here thereof refers to developing computationally- tractable algorithm(s) towards detecting and tracking hostile LoTs and/or drones flying at low altitudes over the sea (at a given range, R) in littoral regions by an LPI radar. Estimation of relevant detection-theoretic parameters and decide track-scores in terms of maximum likelihood (ML) estimates are presented and discussed.

Generalization Ability of Deep Learning Algorithms Trained using SEM Data for Objects Classification

This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra-Wide Band scattered field in time and frequency domains and pre-processed data from the Singularity Expansion Method (SEM). Indeed, the resonances extracted using the SEM are aspect independent and therefore, are used to discriminate the objects. As for the residues, they depend upon the aspect angle and can hence be exploited to determine the observation angle. We construct 3 datasets to assess which one yields the highest accuracy while using the simplest and fastest classifiers. Hence, 80% of each dataset is used for training and the remaining 20% are used for testing. In a further step, we test with sphere sizes and data with several noisy levels that were not in the training datasets. Although SEM is noise sensitive, associating a robust feature extraction technique with a simple but reliable classifier is promising, particularly when generalizing to data not included in the training set.

Generalization Ability of Deep Learning Algorithms Trained using SEM Data for Objects Classification

This paper proposes a workflow to efficiently determine the material of spherical objects and the location of the receiving antenna relative to their position in bi-static measurements using supervised learning techniques. From a single observation, we compare classification performances resulting from the application of several classifiers on different data types: the Ultra-Wide Band scattered field in time and frequency domains and pre-processed data from the Singularity Expansion Method (SEM). Indeed, the resonances extracted using the SEM are aspect independent and therefore, are used to discriminate the objects. As for the residues, they depend upon the aspect angle and can hence be exploited to determine the observation angle. We construct 3 datasets to assess which one yields the highest accuracy while using the simplest and fastest classifiers. Hence, 80% of each dataset is used for training and the remaining 20% are used for testing. In a further step, we test with sphere sizes and data with several noisy levels that were not in the training datasets. Although SEM is noise sensitive, associating a robust feature extraction technique with a simple but reliable classifier is promising, particularly when generalizing to data not included in the training set.

A classification technique of group objects by artificial neural networks using estimation of entropy on synthetic aperture radar images

The article discusses the method for the classification of non-moving group objects for information received from unmanned aerial vehicles (UAVs) by synthetic aperture radar (SAR). A theoretical approach to analysis of group objects can be estimated by cross-entropy using a naive Bayesian classifier. The entropy of target spots on SAR images revaluates depending on the altitude and aspect angle of a UAV. The paper shows that classification of the target for three classes able to predict with fair accuracy P=0,964 based on an artificial neural network. The study of results reveals an advantage compared with other radar recognition methods for a criterion of the constant false-alarm rate (PCFAR<0.01). The reliability was confirmed by checking the initial data using principal component analysis.

Linear Discriminant Analysis-Based Motion Classification Using Distributed Micro-Doppler Radars with Limited Backhaul

In this paper, we propose a cooperative linear discriminant analysis (LDA)-based motion classification algorithm for distributed micro-Doppler (MD) radars which are connected to a data fusion center through the limited backhaul. Due to the limited backhaul, each radar cannot report the high-dimensional data of a multi-aspect angle MD signature to the fusion center. Instead, at each radar, the dimensionality of the MD signature is reduced by using the LDA algorithm and the dimensionally-reduced MD signature can be collected at the data fusion center. To further reduce the burden of backhaul, we also propose the softmax processing method in which the distances of the sensed MD signatures from the centers of clusters for all motion candidates are computed at each radar. The output of the softmax process at each radar is quantized through the pyramid vector quantization with a finite number of bits and is reported to the data fusion center. To improve the classification performance at the fusion center, the channel resources of the backhaul are adaptively allocated based on the classification separability at each radar. The proposed classification performance was assessed with synthetic simulation data as well as experimental data measured through the USRP-based MD radar.

FEKO™ Simulation of Radar Scattering from Objects in Low Earth Orbit for ISAR Imaging

Objects in low earth orbit such as CubeSats and the International Space Station (ISS) move with constant velocity along a linear trajectory when viewed from a ground-based radar. The small change in attitude of the object as it flies overhead permits the generation of an inverse synthetic aperture radar (ISAR) image. In this paper, Altair’s FEKO™ software is used to model the monostatic radar scattering from the ISS as a function of frequency and aspect angle. The computed data is used for generating a simulated ISAR image from a ground-based radar. The system design requirements for the radar are calculated from the radar equation.