Signal-to-clutter-noise Ratio Research Articles

Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

The space–time adaptive processing (STAP) technique can effectively suppress the ground clutter faced by the airborne radar during its downward-looking operation and thus can significantly improve the detection performance of moving targets. However, the optimal STAP requires a large number of independent identically distributed (i.i.d) samples to accurately estimate the clutter plus noise covariance matrix (CNCM), which limits its application in practice. In this paper, we fully consider the heterogeneity of clutter in real-world environments and propose a sparse Bayesian learning-based reduced-dimension STAP method that achieves suboptimal clutter suppression performance using only a single sample. First, the sparse Bayesian learning (SBL) algorithm is used to estimate the CNCM using a single training sample. Second, a novel angular Doppler channel selection algorithm is proposed with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR). Finally, the reduced-dimension STAP filter is constructed using the selected channels. Simulation results show that the proposed algorithm can achieve suboptimal clutter suppression performance in extremely heterogeneous clutter environments where only one training sample can be used.

Joint allocation of transmit-receive resource for MIMO-STAP

Aiming to address the issue of target detection performance degradation due to limited resources of airborne multiple-in multiple-out (MIMO) radar, a joint allocation method of transmit-receive spatial-temporal resources is developed to ameliorate the detection performance of MIMO radar-based space-time adaptive processing (STAP). A MIMO-STAP model containing space-time resources to be configured is firstly constructed. Then, with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR), a problem to jointly design the transmitting and receiving elements, transmitting pulses, baseband waveform, and receiving weight is formulated. To tackle the resultant complex nonlinear joint optimization issue, it is decomposed into four independent sub-problems based on the alternating iteration strategy, then the sub-problems are efficiently solved by corresponding optimization techniques, i.e., the optimal antenna pulse subset can be selected by employing the sequential convex approximation (SCA) method, the transmitting waveform is optimized using the semi-positive definite program (SDP) and randomization method, and the receiving weight is designed under the minimum variance distortionless response (MVDR) criteria, which work iteratively to achieve an effective solution to the joint optimization problem. Extensive simulation results verify the effectiveness of the developed algorithm.

Illuminator scheduling method for passive radar sea surface target detection based on RCS characteristic

ABSTRACT In order to improve the signal-to-clutter-noise ratio (SCNR) of received signal, this letter studies the illuminator scheduling problem in the detection process with the aim of maximizing SCNR time series. In order to use the illuminator with long detection time as much as possible and get high SCNR in the detection process, we propose a neighbourhood search illuminator scheduling method. Firstly, we study the characteristics of radar cross-sections (RCS) of target and sea surface under different incident directions and polarizations, and analyse the regions of high target RCS and low sea surface RCS. Then the coordinate transformation method is used to get the scheduling sequence within the detection range. Based on the global scheduling sequence, the neighbouring frames are searched to obtain the illuminator sequence of the randomly appearing target. Simulation results show that the proposed method can effectively improve the received signal SCNR 15 dB on average.

Target Recognition of SAR Image Based on CN-GAN and CNN in Complex Environment

In recent years, with the rapid development of deep learning, the research of radar image automatic target recognition (ATR) has made great progress. However, because of the complex environments and special imaging principles, Synthetic Aperture Radar (SAR) image still have the problems of sample scarcity and strong speckle noise, which affects the target recognition performance. To solve the above problems, we proposed a target recognition method of SAR image based on Constrained Naive Generative Adversarial Networks (CN-GAN) and Convolutional Neural Network (CNN). Combining Least Squares Generative Adversarial Networks (LSGAN) and Image-to-Image Translation (Pix2Pix), CN-GAN can overcome these problems of low Signal-to-Clutter-Noise Ratio (SCNR), model instability and the excessive freedom degree of the output, which are produced by conventional naive GAN. Besides, we adopted a shallow network structure design in CNN, which can effectively improve the generalization ability of the model and avoid the problem of model overfitting. The experimental results in this paper demonstrate that CN-GAN has achieved the data generation and data enhancement, the SCNR of generated data is higher than the origin data set and data sets gained by other forms of GANs, the recognition performance based on the extended data set is better than the origin data set, and the recognition rate of data set enhanced by CN-GAN is higher than that of other common data enhancement methods.

Circular Scan ISAR Mode Super-Resolution Imaging of Ships Based on a Combination of Data Extrapolation and Compressed Sensing

Airborne radars for maritime surveillance usually operate in scanning mode to detect marine objects over 360 degrees. More recently, the concept of forming inverse synthetic aperture radar (ISAR) images while the antenna is scanning, called circular scan ISAR in this paper, has drawn attention. Circular scan ISAR enables a large coverage area, sequential imaging of multiple targets, and the formation of an image sequence of a target. However, the rapid antenna scanning results in a short accumulation time of the target and reduces the Doppler resolution of the system. This paper analyzes the limit values of the Doppler resolution improvement ratio when super-resolution imaging is performed using only the compressed sensing method by relating the limit values to the restricted isometry constant (RIC) value of the sensing matrices. The limit values are found to be relatively small when the sparsity degree of the sparse signal is relatively large. Therefore, the super-resolution imaging of ships for circular scan ISAR is carried out by extrapolating data in the azimuth direction first and then reconstructing the data via compressed sensing to obtain low side-lobe levels. Finally, the constant false alarm rate (CFAR) detection is performed on the matrix composed of calculated sparse coefficients to improve the signal-to-clutter noise ratio (SCNR). The simulation and real-data processing results are provided to verify the effectiveness of the presented algorithm.

Low‐observable target detection using two‐stage RFRFT

Long-time coherent integration is an effective method to implement low-observable target detection in a complex scenario. Radon-fractional Fourier transform (RFRFT) can compensate the range and Doppler migration simultaneously and obviously improve the signal-to-clutter-noise ratio (SCNR) via long-time coherent integration. However, RFRFT uses a combination of moving parameters, i.e. initial range, velocity and acceleration, to search targets, increasing the computational burden. In this study, a novel two-stage RFRFT is proposed to detect low-observable targets. In the first stage, by calculating the spectrum similarity between adjacent bins, regions of interest (ROIs) can be detected in the range–Doppler domain. The processing in the first stage reduces the global search to a limited number of ROIs. In the second stage, based on the extraction of moving parameters, a refined search is performed to realise target detection and false alarm discrimination. To verify the effectiveness of the proposed two-stage method, both a simulation scenario and real radar dataset are applied. The theoretical analysis and experiment results show that the novel method is effective for the detection of dim targets with far less computation than standard RFRFT.

Research on Clutter Suppression for Low-altitude Slow and Small Target Detection

In order to solve the problem that current clutter suppression methods are not suitable for the clutter suppression in low-altitude slow and small target detection, the method of Filter DC (Direct Current) was proposed in the paper. This method can effectively suppress the clutter by forming a narrower notch filter, while protecting the low-altitude slow and small targets from being affected. In this method, the improvement in Signal-to-Clutter-Noise ratio (SCNR) in different clutter environments was simulated, which proved the superiority of the filter. In this paper, a clutter suppression method was proposed based on noise subspace for motion clutter. Compared with the feature vector method, this method has less influence on the noise of different Doppler channels, and it is more suitable for the detection of low-altitude slow and small targets. Finally, the feasibility and effectiveness of the algorithms were proved by real unmanned aerial vehicle data.

Transmit Waveform Optimization for Spatial-Frequency Diversity MIMO Radar in the Presence of Clutter

Benefitting from the independent target echoes of diversity channels, diversity MIMO radar can efficiently improve system performance, such as target detection and parameter estimation. Due to the fact that the RCS (radar cross section) of complex target may vary with the different transmitted carrier frequencies and array geometries, many recent researches study at the background of diversity MIMO radar equipped with widely separated array antennas or working at multiple carrier frequencies, respectively. In this paper, a new MIMO radar system combining the spatial and frequency diversities is investigated in the presence of signal-dependent clutter, which is called spatial-frequency diversity MIMO radar. With the prior information of target and clutter, a new method for joint optimization of transmitted waveforms and receiving filters is proposed to enhance the target detection ability of spatial-frequency diversity MIMO radar. Inspired by the MIMO communication system, the water-filling algorithm is introduced into the transmitted energy allocation problem for each carrier frequency channel. Simulation results show that the proposed system has a better performance in output signal-to-clutter-noise ratio (SCNR) compared to conventional diversity MIMO radar system.