Synthetic aperture radar (SAR) is commonly used for ship imaging on the sea. By using small target detection algorithms, ship targets can be highlighted under different SAR backgrounds for detection and observation. Local contrast measurement (LCM) has poor detection performance in situations with strong background noise or uneven distribution, and its results cannot preserve the shape features of the original targets well. Inspired by LCM, this paper proposes a discrete cosine transform (DCT) based local contrast enhancement detection algorithm. This algorithm enhances the target area according to AC coefficient, and experimental verification and analysis show that the proposed algorithm has better detection performance and higher level of precision than LCM in the presence of complex background noise.

Reasonable analysis of radar interference effect is of great significance for adjusting jamming strategy in radar counter-measures (RCM). The modern battlefield is confronted with non-cooperative targets, so the conventional offline evaluation methods are difficult to apply. In this paper, a comprehensive evaluation method for radar interference effect based on the integrated cloud model is proposed. Firstly, a multi-layer index system for interference effect evaluation is established. Subsequently, the entropy method is employed to determine the weight of each indicator. To avoid the occurrence of hypertrophy as an imaginary number, the cloud parameters for each indicator are calculated using a modified inverse cloud generator. Eventually, a comprehensive assessment of the interference effect can be obtained by drawing the integrated cloud. The experimental results show that the proposed method is effective and can be applied to the evaluation of interference effectiveness in non-cooperative environments.

In this paper, the constrained waveform design of multiple-input multiple-output (MIMO) radar is considered to achieve transmit beampattern assignment. Firstly, we construct a framework that minimizes the spatial integrated sidelobe level ratio (ISLR) as the objective function and constrains the transmit waveforms in terms of amplitude fluctuations and similarity. To solve the resulting non-convex problem, an iterative optimization method based on coordinate descent (CD) is developed by transforming the multivariate problem into multiple univariate problems. Finally, numerical simulation results demonstrate the effectiveness of the proposed method in beampattern assignment and waveform similarity.

Modern warfare has entered the era of information and networking, where electronic warfare (EW) is of vital importance. Operation mode recognition occupies an important po-sition in EW, while the overlapping waveform parameters of airborne radar operation modes make it difficult to accom-plish the recognition task in complex electromagnetic environments' especially under low signal-to-noise ratio (SNR) regions. Analyzing the time-sequential regularity of radar pulse parameters and intermediate frequency (IF) sampling signals, this paper designs a novel representation of operation modes, and proposes a multi-feature residual-and-shrinkage ConvNet (RS-ConvNet) with an attention mechanism to iden-tify multiple air-to-air modes. Simulation results show the proposed method has superior performance under low SNRs.

Multiple-input multiple-output (MIMO) radar beampattern design has aroused extensive attention, in view of the improved detection capability, the enhanced system adaptiveness and the controlled spatial energy distribution. In addition, the ambiguity function of transmit waveform is also an important indicator of radar system performance. Thus, jointly considering the radar beampattern design and waveform similarity constraints, this paper applies the coordinate descent (CD) algorithm framework to develop an indirect ripple control approach, where beampattern ripple suppression can effectively reduce target distortion. Numerical simulation results verify the effectiveness of the proposed method in controlling ripple and similarity.