Conventional least mean square (LMS) algorithm is one of the most popular algorithms, which is widely used for adaptive beamforming. But the performance of the LMS algorithm degrades significantly because the constant step size is not suitable for varying signal-to-noise ratio (SNR) scenarios. Although numerous variable step size LMS (VSS-LMS) algorithms were proposed to improve the performance of the LMS algorithm; however, most of these VSS-LMS algorithms are either computationally complex or not reliable in practical scenarios since they depend on many parameters that are not easy to tune manually. In this letter, a fast and robust VSS-LMS algorithm is proposed for adaptive beamforming. The variable step size is obtained based on normalized sigmoid function, where the sigmoid function is calculated by using the mean of instantaneous error firstly and then normalized by the squared cumulative sum of instantaneous error and estimated signal power. The proposed algorithm can update the step size adaptively without tuning any parameter and outperform state-of-the-art algorithms with low computational complexity. The simulation results show better performance of the proposed algorithm.

Pulse compression, usually achieved using a matched filter (MF), is extensively applied in radar/sonar systems. This process improves the signal-to-noise ratio (SNR) of the received signal without increasing the peak transmitted power. However, it is often desirable to accept a slight penalty in the target SNR in order to suppress the range sidelobes. In this paper, a novel weighted mismatched filter (WMMF) that can reduce range sidelobes to the designed level over specified regions is proposed. Two algorithms are provided to calculate the WMMF in different situations. A novel performance metric, the mean sidelobe level over a specified region (MSLSR), is also proposed to demonstrate the performance of the proposed method. Then, the SNR loss is deduced and analyzed based on several experiments. Finally, simulations and experiments based on real measured data are conducted to illustrate the effectiveness of this method. The theoretical evaluation and experiments show that the proposed WMMF can be used for all types of waveforms based on pulse compression processing and can flexibly and effectively suppress range sidelobes. Moreover, the mainlobe and the SNR loss can also be controlled by the proposed algorithms. In addition, this method can be easily applied to existing radar systems without modifying the radar transmitter or receiver.

Migration is a key process in the population dynamics of numerous insect species, including many that are pests or vectors of disease. Identification of insect migrants is critically important to studies of insect migration. Radar is an effective means of monitoring nocturnal insect migrants. However, species identification of migrating insects is often unachievable with current radar technology. Special-purpose entomological radar can measure radar cross-sections (RCSs) from which the insect mass, wingbeat frequency and body length-to-width ratio (a measure of morphological form) can be estimated. These features may be valuable for species identification. This paper explores the identification of insect migrants based on the mass, wingbeat frequency and length-to-width ratio, and body length is also introduced to assess the benefit of adding another variable. A total of 23 species of migratory insects captured by a searchlight trap are used to develop a classification model based on decision-tree support vector machine method. The results reveal that the identification accuracy exceeds 80% for all species if the mass, wingbeat frequency and length-to-width ratio are utilized, and the addition of body length is shown to further increase accuracy. It is also shown that improving the precision of the measurements leads to increased identification accuracy.

High-resolution range profile (HRRP) is increasingly employed in radar target recognition under intricate ground scenarios. Such scenarios demand recognizing the specific type of a target from a wide range of categories, a task known as fine-grained target recognition (FGTR), which involves numerous and potentially unbalanced categories. To tackle this, we propose a joint semantic-data guided hierarchical classification (SDHC) framework. It consists of a set of local classifiers organized in a tree hierarchy based on the joint semantic-data relationship. It allows the complex FGTR task to be simplified into multiple small-scale sub-tasks. Specifically, the proposed SDHC method focuses on tree hierarchy construction and local classifier training. We design the tree hierarchy based on a joint semantic-data similarity measure, which quantifies the data similarity between categories and incorporates semantic knowledge constraints. Following this, we deploy hierarchical feature selection on a multi-dimensional feature set, considering the contribution of features in each local classifier. Experimental results on measured data verify the effectiveness of the proposed method. Moreover, analysis results demonstrate the superiority of the hierarchical approach over flat methods.

Inverse synthetic aperture radar (ISAR) is capable of producing two-dimensional (2D) and three-dimensional (3D) images of non-cooperative targets. Compared with 2D ISAR images, 3D ISAR reconstructions can provide not only range and cross-range information, but also the information about the third dimension of the target, which is of great significance for automatic target recognition (ATR) and even more specifically to the case of non-cooperative target recognition (NCTR). The alignment between the point-like 3D ISAR reconstructions of targets and the targets' models, such as CAD models, becomes one of the essential issues in ATR that uses 3D ISAR reconstructions. In this paper, we propose an approach to address the alignment problem. The alignment problem can be decomposed into two steps, a coarse alignment and an accurate alignment. The coarse alignment can be accomplished by means of the principal component analysis (PCA), while the accurate alignment can be achieved by iterative closest point (ICP) algorithm. The 180- degree ambiguity issue in PCA is discussed and a corresponding robust solution is proposed in this paper. Moreover, the k-d tree is utilized to accelerate the searching for the pairs of correspondences, both in coarse and accurate alignment steps. In order to simulate real radar scenarios, target self-occlusion is also taken into consideration. The experimental results based on target's scattering point model simulation as well as the electromagnetic simulation in different radar scenarios verify the validity of the proposed method.

At present, the raw echo data captured by spaceborne SAR is usually downlinked to the ground stations for imaging and analysis. If the SAR data can be processed on-board, the processing results could be transmitted to users directly through broadcast distribution, which can greatly reduce the delay time for emergency applications. However, on-board processing implementation faces many challenges due to the space radiation environment and limited resources of the satellite. In this paper, an FPGA-based implementation of ship detection for on-board processing is proposed, which has high efficiency in logic and memory resources. First, the hardware-oriented clustering approach is proposed. Image processing operations with similar regularity of data access can be mapped in one processing engine, which can reduce the employment of logic resources. Second, the concept of the data-buffering cycle (DBC) is proposed. The DBC provides a method to analyze the intermediate data and optimize the memory reuse. Finally, based on the above optimization methods, the FPGA-based implementation for a ship detection algorithm is presented. Compared with the traditional methods, the experimental results show the efficacy of our proposed method with lower consumption in logic and memory resources.