MIMO-Based Forward-Looking SAR Imaging Algorithm and Simulation

Multiple-input multiple-output (MIMO) radar imaging can provide higher resolution and better sensitivity and thus can be applied to targets detection, recognition, and tracking. Missile-borne forward-looking SAR (MFL-SAR) is a new and special MIMO radar mode. It has advantage of two-dimensional (2D) imaging capability in forward direction over monostatic missile-borne SAR and airborne SAR. However, it is difficult to obtain accurate 2D frequency spectrum of the target echo signal due to the high velocity and descending height of this platform, which brings a lot of obstacles to imaging algorithm design. Therefore, a new imaging algorithm for MFL-SAR configuration based on the method of series reversion is proposed in this paper. This imaging method can implement range compression, secondary range compression (SRC), and range cell migration correction (RCMC) effectively. Finally, some simulations of point targets and comparison results confirm the efficiency of our proposed algorithm

FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary

This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such as graphics processing units (GPUs) and Digital Signal Processors (DSPs). However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP) algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT) and the large scale least square problem is implemented by Conjugate Gradient (CG) technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications

SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification

Conventional classification algorithms have shown great success in balanced hyperspectral data classification. However, the imbalanced class distribution is a fundamental problem of hyperspectral data, and it is regarded as one of the great challenges in classification tasks. To solve this problem, a non-ANN based deep learning, namely SMOTE-Based Weighted Deep Rotation Forest (SMOTE-WDRoF) is proposed in this paper. First, the neighboring pixels of instances are introduced as the spatial information and balanced datasets are created by using the SMOTE algorithm. Second, these datasets are fed into the WDRoF model that consists of the rotation forest and the multi-level cascaded random forests. Specifically, the rotation forest is used to generate rotation feature vectors, which are input into the subsequent cascade forest. Furthermore, the output probability of each level and the original data are stacked as the dataset of the next level. And the sample weights are automatically adjusted according to the dynamic weight function constructed by the classification results of each level. Compared with the traditional deep learning approaches, the proposed method consumes much less training time. The experimental results on four public hyperspectral data demonstrate that the proposed method can get better performance than support vector machine, random forest, rotation forest, SMOTE combined rotation forest, convolutional neural network, and rotation-based deep forest in multiclass imbalance learning

Label Noise Cleaning with an Adaptive Ensemble Method Based on Noise Detection Metric

International audienceReal-world datasets are often contaminated with label noise; labeling is not a clear-cut process and reliable methods tend to be expensive or time-consuming. Depending on the learning technique used, such label noise is potentially harmful, requiring an increased size of the training set, making the trained model more complex and more prone to overfitting and yielding less accurate prediction. This work proposes a cleaning technique called the ensemble method based on the noise detection metric (ENDM). From the corrupted training set, an ensemble classifier is first learned and used to derive four metrics assessing the likelihood for a sample to be mislabeled. For each metric, three thresholds are set to maximize the classifying performance on a corrupted validation dataset when using three different ensemble classifiers, namely Bagging, AdaBoost and k-nearest neighbor (k-NN). These thresholds are used to identify and then either remove or correct the corrupted samples. The effectiveness of the ENDM is demonstrated in performing the classification of 15 public datasets. A comparative analysis is conducted concerning the homogeneous-ensembles-based majority vote method and consensus vote method, two popular ensemble-based label noise filters

A Non-Uniform Interrupted-Sampling Repeater Jamming Method for Intra-Pulse Frequency Agile Radar

The existing research proposes an intra-pulse frequency agile radar waveform with “active” anti-jamming characteristics. It uses the discontinuity and periodicity of the interrupted-sampling repeater jamming and combines the anti-jamming algorithm to effectively suppress interrupted-sampling repeater jamming. In order to improve the jamming effectiveness of the interferer for the intra-pulse frequency agile waveform, this paper proposes to jam the intra-pulse frequency agile radar by using a non-uniform interrupted-sampling and forwarding method under parameter constraints. The proposed method first obtains the sub-pulse width of the intra-pulse frequency agile radar waveform by parameter estimation of the intercepted intra-pulse frequency agile radar signal through time–frequency ridge extraction and wavelet transform. Then, we construct non-uniform interrupted-sampling repeater jamming based on sub-pulse width constraint interference parameters. Theoretical analysis and results show that the non-uniform interrupted-sampling forwarding under parameter constraints makes it challenging to suppress interference in multiple domains, such as the time–frequency and pulse compression domain for intra-pulse frequency agile radar, which significantly improves the jamming capability of the jammer for intra-pulse frequency agile radar

Super-Resolution Range and Velocity Estimations for SFA-OFDM Radar

Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method

Super-Resolution Range and Velocity Estimations for SFA-OFDM Radar

Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method

A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data

In recent years, airborne Light Detection and Ranging (LiDAR) that provided three-dimensional forest information has been widely applied in forest inventory and has shown great potential in automatic individual tree crown delineation (ITCD). Usually, ITCD algorithms include treetop detection and crown boundary delineation procedures. In this study, we proposed a novel method called region-based hierarchical cross-section analysis (RHCSA), which combined the two procedures together based on a canopy height model (CHM) derived from airborne LiDAR data for ITCD. This method considers the CHM as a three-dimensional topological surface, simulates stereoscopic scanning from top to bottom using an iterative process, and utilizes the individual crown and vertical structure of crowns to progressively detect individual treetops and delineate crown boundaries. The proposed method was tested in natural forest stands with high canopy densities in Liangshui National Nature Reserve and Maoershan Forest Farm, Heilongjiang Province, China. Its performance was evaluated by an accuracy procedure that considered both the relative position of treetops and overlapped area of crowns. The average overall accuracy achieved was 85.12% for coniferous plots, 83.86% for deciduous plots and 86.44% for coniferous and broad-leaved mixed forest plots. The results revealed that the RHCSA method can detect and delineate individual tree crowns with little influence from forest types and crown size. It could provide technical support for individual tree crown delineation in coniferous, deciduous and mixed forests with high canopy densities

High Speed Maneuvering Platform Squint TOPS SAR Imaging Based on Local Polar Coordinate and Angular Division

This paper proposes an imaging algorithm for synthetic aperture radar (SAR) mounted on a high-speed maneuvering platform with squint terrain observation by progressive scan mode. To overcome the mismatch between range model and the signal after range walk correction, the range history is calculated in local polar format. The Doppler ambiguity is resolved by nonlinear derotation and zero-padding. The recovered signal is divided into several blocks in Doppler according to the angular division. Keystone transform is used to remove the space-variant range cell migration (RCM) components. Thus, the residual RCM terms can be compensated by a unified phase function. Frequency domain perturbation terms are introduced to correct the space-variant Doppler chirp rate term. The focusing parameters are calculated according to the scene center of each angular block and the signal of each block can be processed in parallel. The image of each block is focused in range-Doppler domain. After the geometric correction, the final focused image can be obtained by directly combined the images of all angular blocks. Simulated SAR data has verified the effectiveness of the proposed algorithm