Aiming at the problem of high computational complexity of adaptive beamforming techniques in array radar systems, a two-level weighted low-complexity adaptive beamforming method is proposed in this letter. First, the uniform linear array is divided into subarrays and each subarray has the same elements. The desired signal received by each array element in the subarray is then superimposed by compensating for the delay using a first level weighting. Finally, the interference signals and noise are suppressed using a second level weighting to obtain the ideal output signal to interference plus noise ratio. Simulation results verify the effectiveness and reliability of the proposed method.

Accurate, timely and wide-ranging radar Quantitative Precipitation Estimation (QPE) is crucial for effective water resource management and climate research. However, the insufficient consideration of surface meteorological conditions such as moisture information leads to significant biases for existing QPE methods. This paper proposes a novel quantitative precipitation estimation network based on hierarchical multi-branch convolutional blocks (MCB-Net). The network incorporates relative humidity data and radar data into a multimodal-fusion architecture, effectively extracting information from diverse data sources and correcting precipitation loss due to evaporation. Additionally, a series of MCB blocks are designed to replace the traditional convolutional operation with multi-branch convolutional units, enabling MCB-Net to capture complicated spatial and temporal features related to precipitation. The indices including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Ratio (MBR) and Correlation Coefficient (CC) are adopted to evaluate the performance of the proposed method. Experimental results demonstrate that MCB-Net outperforms conventional method and other deep learning based QPE networks including Volume-to-Point CNN network, U-Net and ResNet. The visualized results illustrate that the proposed network can effectively mitigate precipitation overestimation and enhance the accuracy of precipitation estimation.

Abstract At present, there are few sparse arrays used in the mixed near‐field (NF) and far‐field (FF) localization based on non‐circular (NC) signals. Inspired by the symmetric flipped nested array (SFNA) used in the existing mixed NF and FF NC source, in order to further improve the parameter estimation accuracy of the mixed NF and FF NC signal, an improved symmetric flipped nested array (ISFNA) for mixed NF and FF NC sources localization was developed. First, the uniform subarrays in the SFNA are rearranged, elements are extracted from the uniform subarrays and rearranged into ISFNA. ISFNA is more sparse, the array aperture is larger, and the array degree of freedom (DOF) is higher; second, the formula of the maximum consecutive lags of ISFNA is given; third, a special fourth‐order cumulant is used to eliminate the range parameter and then use a one‐dimensional (1‐D) spectral peak search to obtain all Directions of Arrival (DOAs). By defining the range search, the range can be obtained by bringing in estimated DOAs. Finally, the superiority of the proposed array is proved by simulation.

The existing angle-based localization methods are mainly suitable for the single source. Actually, there often exists situation which contains multiple target sources. To solve the problem of localization of multitarget sources, this paper presents a K-means clustering method based on multiple screening, which can effectively realize the localization of multiple sources based on DOA (direction of arrival) parameters. The method firstly establishes a cost function of position coordinates by using DOA parameters from the measuring position coordinates and then solves the cost function to obtain a complete set of real position coordinates and fuzzy position coordinates. As the distribution of real target coordinates is concentrated and the fuzzy target positions are scattered, the K-means clustering method is adopted to classify the coordinate set. In order to improve the positioning accuracy, a multiscreening process is introduced to screen the input samples before each clustering, and it can be finally concluded that clustering centers are the position coordinates of the target sources. Meanwhile, the complexity analysis and performance verification of this method are proposed. Simulation experiments show that this method can efficiently realize ambiguity-free, highly precise localization of multitarget sources.

The study suggests a new non‐line of sight (NLOS) error mitigation algorithm based on Kalman filter and neural network. According to the construction features of the former and the statistic characteristics of the latter, the study theoretically deduces the condition to obtain the unbiased estimation of the true value of TOA, and then the study fixes on the state transition matrix of Kalman filter with neural network in different environment. The simulation results based on wideband code division multiple access (WCDMA) experimental data show that the location performance is improved with better estimation accuracy and robustness.