Long-time coherent integration technique is one of the most important methods for the improvement of radar detection ability of a weak maneuvering target, whereas the integration performance may be greatly influenced by the across range unit (ARU) and Doppler frequency migration (DFM) effects. In this paper, a novel representation known as Radon-fractional Fourier transform (RFRFT) is proposed and investigated to solve the above problems simultaneously. It can not only eliminate the effect of DFM by selecting a proper rotation angle but also achieve long-time coherent integration without ARU effect. The RFRFT can be regarded as a special Doppler filter bank composed of filters with different rotation angles, which indicates a generalization of the traditional moving target detection (MTD) and FRFT methods. Some useful properties and the likelihood ratio test detector of RFRFT are derived for maneuvering target detection. Finally, numerical experiments of aerial target and marine target detection are carried out using simulated and real radar datasets. The results demonstrate that for integration gain and detection ability, the proposed method is superior to MTD, FRFT, and Radon-Fourier transform under low signal-to-clutter/noise ratio (SCR/SNR) environments. Moreover, the trajectory of target can be easily obtained via RFRFT as well.

Maneuvering Target Detection via Radon-Fractional Fourier Transform-Based Long-Time Coherent Integration

From the theory of compressive sensing (CS), we know that the exact recovery of an unknown sparse signal can be achieved from limited measurements by solving a sparsity-constrained optimization problem. For inverse synthetic aperture radar (ISAR) imaging, the backscattering field of a target is usually composed of contributions by a very limited amount of strong scattering centers, the number of which is much smaller than that of pixels in the image plane. In this paper, a novel framework for ISAR imaging is proposed through sparse stepped-frequency waveforms (SSFWs). By using the framework, the measurements, only at some portions of frequency subbands, are used to reconstruct full-resolution images by exploiting sparsity. This waveform strategy greatly reduces the amount of data and acquisition time and improves the antijamming capability. A new algorithm, named the sparsity-driven High-Resolution Range Profile (HRRP) synthesizer, is presented in this paper to overcome the error phase due to motion usually degrading the HHRP synthesis. The sparsity-driven HRRP synthesizer is robust to noise. The main novelty of the proposed ISAR imaging framework is twofold: 1) dividing the motion compensation into three steps and therefore allowing for very accurate estimation and 2) both sparsity and signal-to-noise ratio are enhanced dramatically by coherent integrant in cross-range before performing HRRP synthesis. Both simulated and real measured data are used to test the robustness of the ISAR imaging framework with SSFWs. Experimental results show that the framework is capable of precise reconstruction of ISAR images and effective suppression of both phase error and noise.

High-Resolution ISAR Imaging With Sparse Stepped-Frequency Waveforms

In order to effectively detect moving targets in heavy sea clutter, the micro-Doppler (m-D) effect is studied and an effective algorithm based on short-time fractional Fourier transform (STFRFT) is proposed for target detection and m-D signal extraction. Firstly, the mathematical model of target with micromotion at sea, including translation and rotation movement, is established, which can be approximated as the sum of linear-frequency-modulated signals within a short time. Then, due to the high-power, time-varying, and target-like properties of sea spikes, which may result in poor detection performance, sea spikes are identified and eliminated before target detection to improve signal-to-clutter ratio (SCR). By taking the absolute amplitude of signals in the best STFRFT domain (STFRFD) as the test statistic, and comparing it with the threshold determined by a constant false alarm rate detector, micromotion target can be declared or not. STFRFT with Gaussian window is employed to provide time-frequency distribution of m-D signals, and the instantaneous frequency of each component can be extracted and estimated precisely by STFRFD filtering. In the end, datasets from the intelligent pixel processing radar with HH and VV polarizations are used to verify the validity of this proposed algorithm. Two shore-based experiments are also conducted using an X-band sea search radar and an S-band sea surveillance radar, respectively. The results demonstrate that the proposed method not only achieves high detection probability in a low-SCR environment but also outperforms the short-time Fourier transform-based method.

Detection and Extraction of Target With Micromotion in Spiky Sea Clutter Via Short-Time Fractional Fourier Transform

A novel feature extraction method based on micro-Doppler signature is proposed to categorize ground moving targets into three kinds, i.e., single walking person, two people walking, and a moving wheeled vehicle. Signal models and measured data from a low-resolution radar are first analyzed to find the differences between the micro-Doppler signatures from the three kinds of considered targets. Then, such discriminative micro-Doppler signatures are represented by a 3-D feature vector extracted from the time-frequency spectrograms. In the experiments based on the measured data, the ratio of the between-class distance to the within-class distance, which is defined based on Fisher discriminant analysis, is exploited to assess the discriminative ability of the 3-D feature vector. Moreover, support vector machine classifier is utilized to evaluate the classification performance. Experimental results show that the proposed micro-Doppler features can achieve a good discriminative ability and a satisfactory classification performance.

Micro-Doppler Feature Extraction Based on Time-Frequency Spectrogram for Ground Moving Targets Classification With Low-Resolution Radar

The rotation, vibration, or coning motion of a target may produce periodic Doppler modulation, which is called the micro-Doppler phenomenon and is widely used for target classification and recognition. In this paper, the signal of interest is decomposed into a family of parametric basis-signals that are generated by discretizing the micro-Doppler parameter domain and synthesizing the micro-Doppler components with over-complete time–frequency characteristics. In this manner, micro-Doppler parameter estimation is converted into the problem of sparse signal recovery with a parametric dictionary. This problem can be considered as a specific case of dictionary learning, i.e., we need to solve for both the sparse solution and the parameter inside the dictionary matrix. To solve this problem, a novel pruned orthogonal matching pursuit (POMP) algorithm is proposed, in which the pruning operation is embedded into the iterative process of the orthogonal matching pursuit (OMP) algorithm. The effectiveness of the proposed approach is validated by simulations.

Micro-Doppler Parameter Estimation via Parametric Sparse Representation and Pruned Orthogonal Matching Pursuit

The micro-Doppler (m-D) effect induced by the rotating parts or vibrations of the target provides a new approach for target recognition. To obtain high range resolution for the extraction of the fine m-D signatures of an inverse synthetic aperture radar target, the stepped-frequency chirp signal (SFCS) is used to synthesize the ultrabroad bandwidth and reduce the requirement of sample rates. In this paper, the m-D effect in SFCS is analyzed. The analytical expressions of the m-D signatures, which are extracted by an improved Hough transform method associated with time-frequency analysis, are deduced on the range-slow-time plane. The implementation of the algorithm is presented, particularly in those extreme cases of rotating (vibrating) frequencies and radii. The simulations validate the theoretical formulation and robustness of the proposed m-D extraction method.

Micro-Doppler Effect Analysis and Feature Extraction in ISAR Imaging With Stepped-Frequency Chirp Signals

The micromotion of a target will generate a micro-Doppler (m-D) effect in the frequency domain. The m-D effect is regarded as a unique property of the target, which has special significance in target detection, identification, and classification. The classical range-Doppler algorithm cannot obtain a clear inverse synthetic aperture radar (ISAR) image due to the m-D effect induced by micromotion dynamics. The m-D effect induced by periodical micromotion is represented as a sinusoidal modulation in a spectrogram, whereas the Doppler induced by a main body is depicted as the form of a straight line. Therefore, the extraction of an m-D signature is transformed into the separation of a sinusoid and a straight line. The cancellation technique is a classical method for removing ground clutter. Based on the same principle, the cancellation technique is applied to the spectrogram in this letter, which successfully achieves the separation of the m-D signature and gets the clearer ISAR image of the main body. The effectiveness and robustness of the algorithm are proved by simulation results.

Micro-Doppler Signature Extraction and ISAR Imaging for Target With Micromotion Dynamics

In traditional monostatic radar, only the micromotion signatures projected in line-of-sight (LOS) could be extracted from the target's echoes, which are insufficient for accurate target recognition due to the target-aspect sensitivity. In this paper, we propose an algorithm for the three-dimensional micro-Doppler signature extraction using the multi-input multi-output (MIMO) radar technique. The micro-Doppler effect in MIMO radar induced by rotation is analyzed. Profited from the multi-view of MIMO radar, the three-dimensional micro-motion features, which are not sensitive to the target's aspect anymore, are obtained by solving nonlinear multivariable equation systems. Simulations are given to validate the effectiveness of the proposed algorithm.

Three-dimensional micro-doppler signature extraction in MIMO radar

SIMO (Single Input Multiple Output) radar system capitalizes on the receiving diversity, so it can obtain more information about the target and highly improve the target recognition capability of radar. In this paper, the micro-Doppler effect induced by rotation and vibration parts of target in SIMO radar system which transmitting single frequency signal are deduced respectively. Both the micro-Doppler effect induced by rotation and vibration are sinusoidal curves, but their amplitude vary with the selection of different receiving radar. In addition, the translation of the target's body will lead some horizontal shifts to the micro-Doppler curves with different value according to the selected receiving radar. Simulations are presented to validate the theoretic conclusions.

Analysis of micro-doppler effect in SIMO radar

The micro-Doppler (m-D) effect describes the subtle micromotion features of a radar target and provides a new approach for feature extraction and auto radar target recognition. However, the microwave radar cannot provide enough resolution to detect the m-D effect of small targets and long distance targets. In order to obtain high range resolution for the extraction of subtle m-D signatures, inverse synthetic aperture imaging LADAR (ISAIL) is used here. Because the ISAIL uses a frequency modulation continuous wave signal, the m-D effect of ISAIL is different from the microwave radar. In this paper, the m-D effect of ISAIL is analyzed. The features of the m-D signatures in ISAIL are extracted by an improved Hough transform method associated with erosion and dilation operations in binary mathematical morphology. The simulations are given to validate the theoretical analyses and the proposed m-D extraction method. The experiment results show that the ISAIL can offer sufficient information of micromotions when the feature of motions is tiny.

Xian-jiao Liang

Papers

Micro-Doppler Effect Analysis and Feature Extraction in ISAR Imaging With Stepped-Frequency Chirp Signals

Micro-Doppler Signature Extraction and ISAR Imaging for Target With Micromotion Dynamics

Three-dimensional micro-doppler signature extraction in MIMO radar

Analysis of micro-doppler effect in SIMO radar

Micro-Doppler effect analysis and feature extraction in inverse synthetic aperture imaging LADAR imaging