Analysis of micro-Doppler signatures

In many radar applications, knowing a target's present position (angle and range) relative to the radar is not enough. Often one must be able to predict the target's position at some future time. For that, we must also know the target's angular rate and its range rate. Range rate may be determined by one of two general methods. In the first, called range differentiation, the rate is computed on the basis of the change in the measured range with time. In the second and generally superior method, the radar measures the target's doppler frequency - which is directly proportional to the range rate. In this chapter, we will look at both methods briefly. We will then take stock of potential doppler ambiguities and see how they may be resolved.

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

Attentions have been focused on the moving target detection in a high-resolution sea clutter. This study commences with a proposal of median-based estimator to estimate the power spectrum of high-resolution sea clutter by the time series observed in adjacent range cells and time intervals. The estimator provides a robust estimation when just a few aberrant time series happen in observation. Based on the estimator, a block-adaptive clutter suppression filter (BACSF) is designed to suppress the clutter prior to the pulse integration. Then, the residual clutter, the output of the BACSF, is modelled as spherically invariant random vector. Upon applying an adaptive normalised matched filter (ANMF) to the residual clutter, a residual clutter's ANMF detector is derived. Moreover, in high-resolution radar background, considering that the approximately stationary intervals of sea clutter and residual clutter are much shorter than the coherent processing interval, another heuristic block-ANMF detector is proposed. It can integrate more pulses and can achieve better performance than the ANMF detector does. This study concludes with the experiments of simulated target against the real sea clutter. The experimental results demonstrate that, when target's Doppler frequency is beyond strong clutter region, the ANMF detector and block-ANMF detector perform better in the residual clutter than in the clutter.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

The paper considers a method for classifying unmanned aerial vehicles (UAVs) and birds based on radar measurements in various object movement scenarios. The relevance of the problem is explained by the complexity of UAV detection due to its small overall dimensions, high agility, and similar geometry to a bird. To solve the problem, a classification method based on the analysis of micro-Doppler signatures, which reflect the dynamics of object motion, is applied. The scenarios are modelled in the Engee environment, where models of radar system, UAV and birds are developed.

This paper addresses the issue of drones and helicopters classification from radar return using point clouds features. In order to classify helicopters or drones, most published researches estimate the length of blades, the number of rotors, the period of their micro-Doppler signatures or try to extract information from spectrograms (or any other image). Differently this work generates a point cloud from radar return signal. Features are extracted from point cloud and they are used as input of an Artificial Neural Net (ANN) to classify different models of helicopters and drones.

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.

Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.

An interference improvement factor (IIF) is derived for a generalised square-law detector utilised for a radar application. This factor, which is a ratio of two quadratic forms, incorporates the effects of antenna beam shape as the radar beam scans over the target, interference due to receiver noise plus clutter and the target Doppler frequency. The IIF can be maximised to improve target detectability via eigenvalue, eigenvector techniques so that the optimal weights to be applied by a weighted integrator following the square-law device can be determined for an N-pulse batch. Numerical results are presented for a sea surveillance application.

Advanced radar receivers having an instantaneous dynamic range exceeding 90 dB are currently under development. These receivers must operate linearly to achieve satisfactory performance in a severe clutter environment. Presently available, state-of-the-art analog-to- digital converters (ADCs) having sufficiently high sampling rates do not have adequate dynamic ranges to digitize the outputs of these receivers. A technique has been developed that effectively reduces the dynamic range of the input signal prior to digitization. The dynamic range reduction is accomplished through a process that predicts the next radar return signal from the previous return signals, generates a replica waveform, and subtracts this replica waveform from the radar return signal prior to digitization. This process allows the radar return signal to be digitized without distortion by an ADC having a limited dynamic range. The full dynamic range of the radar return signal is then restored by adding the replica waveform to the ADC output. Tests and evaluations using both synthetic and recorded radar data have demonstrated in excess of a 30 dB reduction in the dynamic range of the signal at the ADC input when strong clutter is present.

The micro-Doppler signature of a target can provide more information for target classification and recognition. Battlefield surveillance radar has low Doppler resolution, which provide coarse micro-Doppler signature. In order to get more detailed micro-Doppler signature, high-resolution time-frequency distribution is necessary. In this paper, we use S-Wigner distribution(SWD), compared with STFT, to analysis the micro-Doppler signature of vibratory and three typical ground targets in battlefield.

In this paper, we propose a method which can detect dangerous objects such as pistol and knife concealed on pedestrian via a 77GHz radar. The proposed method uses range-FFT and Doppler-FFT to obtain micro-Doppler signature of the concealed object. We extract features from micro-Doppler signature with motion pattern of the pedestrian. A classifier based on Support Vector Machine (SVM) is implemented to distinguish dangerous objects from all detected objects by micro-Doppler features. We set up an experiment platform which uses a PVC dummy to simulate pedestrian to verify our proposed method. Results show that our proposed method can get accuracy of 89.8% and miss alarm rate of 3% among 200 times of detection.

A millimeter wave (MMW) pseudo random code continuous wave (PRC-CW) radar utilizing Doppler compensation is introduced. The novel hybrid transmitted waveform used in this radar combining single frequency pulse with pseudo random binary phase-coded signal is presented which makes consequent target detection through pulse compression independent of Doppler frequency. Doppler frequencies of targets are firstly estimated based on interpolated FFT in the time span of single frequency pulse and the Doppler compensated signal can be constructed. The effect of Doppler mismatch on the correlation detection can be eliminated by processing the compensated signal using conventional pulse compression-FFT, which makes the radar capable of short-range high-speed target detection. The theoretical analysis and simulation result demonstrate the validity of this technique.

A laser coherent detection system of 1550 nm wavelength was presented, and experimental research on detecting micro-Doppler effect in a dynamic target was developed. In the study, the return signal in the time domain is decomposed into a set of components in different wavelet scales by multi-resolution wavelet analysis, and the components are associated with the vibrational motions in a target. Then micro-Doppler signatures are extracted by applying the reconstruction. During the course of the final data processing frequency analysis and time-frequency analysis are applied to analyze the vibrational signals and estimate the motion parameters successfully. The experimental results indicate that the system can effectively detect micro-Doppler information in a moving target, and the tiny vibrational signatures also can be acquired effectively by wavelet multi-resolution analysis and time-frequency analysis.