Range-Doppler Spectrograms-Based Graph-Relational Mapping for Clutter Rejection in HF Passive Radar

A novel quadrature clutter rejection approach based on the multivariate empirical mode decomposition for bidirectional Doppler ultrasound signals

Detecting concrete internal defects through deep learning analysis of impact echo signals faces two challenges: (1) the traditional signal processing method such as wavelet transform (WT) fails to reflect data-sensitive damage characteristics due to the uncertainty principle and (2) the limited labeled data acquired from real structures impedes network training. To address the first challenge, this paper proposes the WT-based synchrosqueezing transform (WT-SST) for the conversion of time-series data to the time-frequency spectrogram, which can provide effective features for the network in time and frequency domains simultaneously. To overcome the second challenge, numerical simulation data are supplemented for the augment of labeled data. To minimize the effect of data variance between experiments and simulations, this paper uses an unsupervised domain adaptation (DA) network for the transfer training of labeled simulation data (original domain) and unlabeled experimental data (target domain). The DA network extracts domain-invariant features by maximizing the domain recognition error and minimizing the probability distribution distance. The damage probability is calculated by the trained model to produce a 2D defect contour image of concrete specimens, and the three-dimensional visualization of internal defects by estimating the defect depth based on the defect area of contour image. Finally, the recognition precision, recall, F1-score, and accuracy of the model of unsupervised DA network trained by a hybrid dataset reaches 89.4%, 88.4%, 88.9%, and 94.7%, respectively.

We present development, testing, and deployment of a next generation advanced EMI sensor from a cart-based sensing platform to combine the mapping capability of previous digital geophysical survey instruments with the high-resolution discrimination capability of advanced characterization arrays. The intended end users of the system are non-technical field personnel. Therefore, system development, including sensor operation, data acquisition, and post-processing methods were customized for ease-of-use and robust automated discrimination. The EMI sensor employs a multi-axis receiver configuration to produce data sufficient for anomaly discrimination. Platform development and design focused on navigation and EMI sensor integration onto a custom, low-noise, metal-free platform. Data acquisition is via an Android application with emphasis on ease-of-use and real-time quality control (QC) of collected data. Post-processing methods emphasize QC, inversion-based anomaly location estimation, and automated or supervised polarizability-based discrimination methods to produce a prioritized dig list. Streamlining of the detection, clutter rejection and QC methods into the post-processing software module reduces analysis complexity and the time required between sensor data collection and generation of a prioritized dig list. System concept of operations (CONOPs), data collection, QC, and data processing procedures, will be discussed. System performance versus standard digital geophysical mapping sensors in production OCONUS UXO remediation operations will also be discussed.

China mobile multimedia broadcasting (CMMB) is a digital television standard that works in a single-frequency network (SFN) at the ultrahigh frequency band and adopts orthogonal frequency division multiplex modulation with a cyclic prefix (CP-OFDM). This paper investigates the practical feasibility of a CMMB-based passive radar (CPR) in the multipath propagation environment. First, several aspects involving system considerations induced by propagation conditions and radar signal characteristics are analyzed, including power budget, dynamic range requirement, clutter rejection metrics, and ambiguity function analysis. Then targeting the problems caused by multipath signals in the complex propagation environment and corresponding processing techniques are discussed, covering reference signal extraction and clutter rejection, as well as, tracking and localization. The highlight is clutter rejection with subcarrier-based spatial adaptive processing (SSAP), which is matched with the features of CP-OFDM. The applicability of SSAP under severe channel response spread mainly contributed by SFN is specially considered. Last, a description of the experiments and the CPR systems developed by Wuhan University is given, together with results from previous field trials for low-altitude aircraft and ship detection.

This work presents a method for clutter rejection and dim target track detection from infrared (IR) satellite data using neural networks. A high-order correlation method which recursively computes the spatio-temporal cross-correlations between data of several consecutive scans is developed. The implementation of this scheme using a connectionist network is presented. Several important properties of the high-order correlation method which indicate that the resultant filtered images capture all the target information are established. The simulation results obtained with this approach show at least 93% clutter rejection. Further improvement in the clutter rejection rate is achieved by modifying the high-order correlation method to incorporate the target motion dynamics. The implementation of this modified high-order correlation using a high-order neural network architecture is demonstrated. The simulation results indicate at least 97% clutter rejection rate for this method. A comparison is also made between the methods developed here and the conventional frequency domain three-dimensional (3-D) filtering scheme, and the simulation results are provided. >

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (GPU) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power GPU as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar.

Detection and classification of rotorcraft targets are of great significance not only in civil fields but also in defense. However, up to now, it is still difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving targets by micro-Doppler features has become a promising research topic in the modern signal processing field. In this paper, we propose to use the deep convolutional neural networks (DCNNs) in rotorcraft detection and helicopter classification based on Doppler radar signals. We apply DCNN directly to raw micro-Doppler spectrograms for rotorcraft detection and classification. The proposed DCNNs can learn the features automatically from the micro-Doppler signals without introducing any domain background knowledge. Simulated data are used in the experiments. The experimental results show that the proposed DCNNs achieve superior accuracy in rotorcraft detection and superior accuracy in helicopter classification, outperforming the traditional radar signal processing methods.

New Adaptive Clutter Rejection for Ultrasound Color Doppler Imaging: In vivo Study

Clutter rejection is a key technique in digital radio Mondiale (DRM)-based HF passive bistatic radar, because the power of a target signal is many orders of magnitude weaker than the direct-path wave and multipath echoes. Many temporal approaches can be used to reject the multipath clutter. We proposed a novel approach which takes full advantage of characteristic of the transmitted waveform. DRM signal is based on the orthogonal frequency division multiplexing modulation technique. After the temporal signal model in subcarrier domain is analyzed, an approach using recursive least square adaptive filter is introduced to remove unwanted components on each effective subcarrier. It has low computational complexity. Simulation and experiment data verification are carried out, which proved the efficiency of the proposed approach.

Several signal analysis systems reported in the literature have benefited from the integration of artificial intelligence (AI) and signal processing. This paper examines a growing class of these systems in which the AI component is used to control signal-processing operations. Several philosophical arguments are presented to support the development of these so called coupled systems. Three different types of coupled-system control schemes are identified and these are illustrated using examples from the literature. The benefits and improvements to traditional methods of signal processing achieved using coupled control schemes are also identified and are again illustrated with examples.

A method of detecting dim targets in highly-cluttered time- varying image sequences is presented, where reliable clutter rejection is achieved by calibrating the multivariate statistics of a small number of generic space-time filters. The targets have sufficiently low SCR that a track-before- detect method is required. For targets where there is little prior information on velocity, a large number of filters is generally required to achieve a high response relative to the background. In the method described here, instead of applying thresholds to individual filters, joint filter statistics are used to estimate conditional threshold exceedance probabilities. A smaller number of more generic filters are applied, which are not finely tuned to targets but which characterize aspects of both targets and clutter. Potential targets are cued based on a non-parametric estimate of the probability of occurrence of similar clutter. Constant false alarm rates are inherent in the method. The method is demonstrated on examples of real forward-looking imagery of the sea surface, where glint is a source of strong clutter. Dim targets are distinguished form clutter by using the joint statistics of three variables: a constant-intensity blob filter, a filter tuned to sea glint flashes, and the vertical image coordinate.

The components in an engineering signal may overlap in the frequency domain, which causes great inconvenience and even errors in traditional one-dimensional signal processing methods. To identify and extract components in the signal from the time-frequency domain, this paper proposes a time-frequency domain signal decomposition method based on enhanced seeded region growing (ESRG). This paper extends the seeded region growing method to adaptive recognition of target regions in the time-frequency representation and divides the time-frequency representation into several time-frequency components. ESRG can reduce the influence of noise, expand the self-adaptability of time-frequency decomposition and avoid errors caused by manually setting the growth point and threshold. The results of the numerical verification and decomposition of bat echo signals show that this method can accurately decompose multi-component non-stationary signals. The decomposition results of rotor fault and rolling bearing fault signals show that this method can be successfully applied to fault diagnosis.

Dynamical systems generate complex signals. Very often, traditional sonar signal processing in both the time domain and frequency domain are not adequate enough to characterize the complex dynamics of underwater sources that generate, radiate and/or reflect sonar signals. Recent advances in signal processing are aimed at capitalizing on the inherent complexities in a signal structure by applying information theoretic/chaotic processing analysis tools to reveal signal dynamics, otherwise unseen when traditional (linear) signal processing methods are applied. Here examples are presented of periodicities (i.e., limit cycles), quasi-periodicites (2-torus and n-torus motion) and nonperiodic (chaotic) motion as applied to signals-of-interest in underwater acoustic applications. It is demonstrated how nonlinear diagnostic tools, when appropriately applied to underwater acoustic time series measurements, can be used for extracting new information about system dynamics.

Big brown bats (Eptesicus fuscus) emit frequency-modulated (FM) biosonar sounds containing two or more harmonic sweeps. Echoes from frontally located targets arrive with first and second harmonics intact, leading to focused delay images. Echoes from offside or distant objects arrive with the second harmonic relatively weaker (lowpass-filtered), leading to defocused images, which prevents their clutter interference effects (Bates et al. J Exp Biol 214:394-401, 2011). Realistic targets contain several glints at slightly different distances and reflect several echoes at correspondingly different delays. The bat registers the delay of the nearest glint's echoes in the time domain. The delays of echoes from the farther glints are registered in the frequency domain, from interference nulls in the spectrum. Lowpass-filtering of echoes directly affects the image of the nearest glint by defocusing the delay image. However, lowpass-filtering also is superimposed on the interference spectrum used to register the farther glints, which distorts the pattern of interference nulls, defocusing the farther glints inversely, in the spectral domain, before they are perceived as delays. Differences in blurring between time-domain and frequency-domain parts of images identifies separate computational paths to perceptually reconstruct objects and prevent interference from off-side or distant clutter.

A new approach to correlation filters based on quadratic distance calculations is described. The problem of distortion tolerance is addressed in terms of similarity measures. Discrimination is simultaneously addressed by optimizing the filters to maximally separate the classes. Mathematically, filter synthesis requires the inversion of diagonal matrices in the frequency domain and is a generalization of the MACE idea. The approach is shift-invariant, does not require feature extraction or image registration, and is significantly different from traditional pattern recognition techniques such as the Fisher LDF. The proposed approach is also suitable for the rejection of unknown clutter. Since recognition is based on similarity, clutter and false images which exhibit `large' distances from true target classes, are easily rejected. With improved recognition and discrimination performance, and low false alarm rates, the proposed distance classifier is a promising method for multiclass target recognition in cluttered environments.