Time-Frequency Analysis.

Big Data for Internet of Things: A Survey

In some applications, it is necessary to analyze multi-component non-stationary signals whose components severely overlap in the time-frequency (T-F) domain. Separating those signal components is desired but very challenging for existing methods. To address this issue, we propose a novel non-parametric algorithm called ridge path regrouping (RPRG) to extract the instantaneous frequencies (IFs) of the overlapped components from a T-F representation (TFR). The RPRG first detects the ridges of a multi-component signal from a TFR and then extracts the desired IFs by regrouping the ridge curves according to their variation rates at the intersections. After the IFs are obtained, component separation is achieved by using the intrinsic chirp component decomposition (ICCD) method. Different from traditional T-F filter-based methods, the ICCD can accurately reconstruct overlapped components by using a joint-estimation scheme. Finally, applications of separating some simulated and experimental micro-Doppler signals are presented to show the effectiveness of the method.

Separation of Overlapped Non-Stationary Signals by Ridge Path Regrouping and Intrinsic Chirp Component Decomposition

A compressive sensing (CS) approach for nonstationary signal separation is proposed. This approach is motivated by challenges in radar signal processing, including separations of micro-Doppler and main body signatures. We consider the case where the signal of interest assumes sparse representation over a given basis. Other signals present in the data overlap with the desired signal in the time and frequency domains, disallowing conventional windowing or filtering operations to be used for desired signal recovery. The proposed approach uses linear time-frequency representations to reveal the data local behavior. Using the L-statistics, only the time-frequency (TF) points that belong to the desired signal are retained, whereas the common points and others pertaining only to the undesired signals are deemed inappropriate and cast as missing samples. These samples amount to reduced frequency observations in the TF domain. The linear relationship between the measurement and sparse domains permits the application of CS techniques to recover the desired signal without significant distortion. We focus on sinusoidal desired signals with sparse frequency-domain representation but show that the analysis can be straightforwardly generalized to nonsinusoidal signals with known structures. Several examples are provided to demonstrate the effectiveness of the proposed approach.

Compressive Sensing Based Separation of Nonstationary and Stationary Signals Overlapping in Time-Frequency

Micro-Doppler signals refer to Doppler scattering returns produced by the motions of the target other than gross translation. The small micro-motions of a subject, and even just parts of a subject, can be observed through the micro-Doppler signature it creates in response to an active emitter such as a radar, laser, and even acoustic emitters. These micro-Doppler signatures are produced by the kinematic properties of the subject's motion and can be used to extract the salient features of the subject's motion, and often, identify the subject. The rapidly declining cost of micro-Doppler-capable active sensors like radar with their dramatically improving capabilities, provide significant motivation in developing micro-Doppler techniques that can improve the exploitation of these sensors. Micro-Doppler techniques aim at extracting the micro-motion of the subject that may be unique to a particular subject class or activity in order to distinguish probable false alarms from real detections, as well as to increase the value of the information extracted from the sensor. The source of micro-motion depends on the subject and can be a rotating propeller on a fixed-wing aircraft, the multiple spinning rotor blades of a helicopter, or an unmanned aerial vehicle (UAV); the vibrations of an engine shaking a vehicle; an antenna rotating on a ship; the flapping wings of birds; the swinging arms and legs of a walking person; and many other sources. Confuser detections, such as birds for UAVs or animals for humans, can be interpreted as false alarms for a sensor system, so using the available micro-Doppler returns for classification can significantly reduce the sensor false alarm rate, thereby improving the utility of the sensor system. This study reviews the current research in micro-Doppler based on subject type, sensor capabilities, as well as environmental effects, and then proposes future research areas for micro-Doppler.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rsn.2015.0118

Review of micro-Doppler signatures

The micro-Doppler (m-D) effect is caused by fast moving reflectors. This effect may significantly decrease the readability of the inverse synthetic aperture radar/synthetic aperture radar (ISAR/SAR) images. An L-statistics-based method for m-D effects removal is proposed. The L-statistics approach is performed on the spectrogram, while the rigid body signal synthesis is done in the complex time-frequency (TF) domain. The proposed method is very simple to use and produces better results than the other TF-based approaches. In addition to being capable of separating the rigid body and the m-D parts, this approach is robust to the noise influence. It may also separate close rigid body points, which are not separated in the original radar image. In the numerical implementation of this approach for radar imaging, the computational efficiency is further improved by using two thresholds. The first threshold determines whether there is a target signal in a range cell, while the second threshold determines whether there are m-D effects in this range cell. These thresholds could significantly decrease the computation time in real-time applications. The theory is illustrated by examples.

Micro-Doppler Removal in the Radar Imaging Analysis

A method for accurate and efficient parameter estimation and decomposition of sinusoidally frequency modulated signals is presented. These kinds of signals are of special interest in radars and communications. The proposed method is based on the inverse Radon transform property to transform a two-dimensional sinusoidal pattern into a single point in a two-dimensional plane. Since the signal is well concentrated (sparse) in the inverse Radon transform domain, its reconstruction can be performed from a reduced set of observations (back-projections). Theory is illustrated on signals with one and more components, including noise and disturbances, as well as time-frequency patterns that deviate from sinusoidal form.

Inverse radon transform–based micro-doppler analysis from a reduced set of observations

A two-stage technique for estimating micro-Doppler signal parameters has been proposed. In the first stage, rough parameter estimations are performed by regression of instantaneous frequency estimate obtained from the short-time Fourier transform. Afterwards, rough estimates are refined in the second stage. The proposed technique has better performance with respect to current state-of-the-art algorithms, and it reaches the Cramer–Rao lower bound for sinusoidal frequency-modulated signal parameters.

The STFT-Based Estimator of Micro-Doppler Parameters

A problem of soil clustering and spatial representation of the obtained results, based on in-situ measurements of physical and chemical characteristics of soil, is analysed in the paper. K-means and fuzzy K-means algorithms are adapted for the soil data clastering. Database of soil samples sampled in Montenegro is used for comparative analysis of the used algorithm. Classified soil data are presented on static Google map.

/pdf/soil-data-clustering-by-using-k-means-and-fuzzy-k-means-4g7jeezts5.pdf

Soil data clustering by using K-means and fuzzy K-means algorithm

The method for detection of complex sinusoids in additive white Gaussian noise and estimation of their frequencies is proposed. It contains two stages: 1) sinusoid detection (model order estimation) and coarse frequency estimation, and 2) fine frequency estimation. The proposed method operates in the frequency domain, i.e., it uses the discrete Fourier transform (DFT) as the main tool. Sinusoid detection is performed so that a fixed probability of false alarm is provided (Neymann–Pearson criterion). For both coarse and fine frequency estimations, the three-point periodogram maximization approach is used. Simulations are carried out for variable signal-to-noise ratio, variable frequency displacement between the sinusoids and variable offset from the frequency grid. The proposed method meets the Cramer-Rao lower bound in frequency estimation and practically does not depend on the frequency displacement except for very small displacement values. In terms of model order estimation accuracy, it outperforms the state-of-the-art approaches. The most expensive operation in the method is the calculation of the DFT. Therefore, in terms of calculation complexity, the proposed method is on par with the most efficient algorithms for multiple frequency estimations.

/pdf/efficient-and-accurate-detection-and-frequency-estimation-of-1i4h45m3nx.pdf

Efficient and Accurate Detection and Frequency Estimation of Multiple Sinusoids

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