In this paper, a system for identifying eight kinds of radar waveforms is explored. The waveforms are the binary phase shift keying (BPSK), Costas codes, linear frequency modulation (LFM) and polyphase codes (including P1, P2, P3, P4 and Frank codes). The features of power spectral density (PSD), moments and cumulants, instantaneous properties and time-frequency analysis are extracted from the waveforms and three new features are proposed. The classifier is support vector machine (SVM), which is optimized by artificial bee colony (ABC) algorithm. The system shows well robustness, excellent computational complexity and high recognition rate under low signal-to-noise ratio (SNR) situation. The simulation results indicate that the overall recognition rate is 92% when SNR is −4 dB.

There is a problem that complex operation which leads to a heavy calculation burden is required when the direction of arrival (DOA) of a sparse signal is estimated by using the array covariance matrix. The solution of the multiple measurement vectors (MMV) model is difficult. In this paper, a real-valued sparse DOA estimation algorithm based on the Khatri-Rao (KR) product called the L₁-RVSKR is proposed. The proposed algorithm is based on the sparse representation of the array covariance matrix. The array covariance matrix is transformed to a real-valued matrix via a unitary transformation so that a real-valued sparse model is achieved. The real-valued sparse model is vectorized for transforming to a single measurement vector (SMV) model, and a new virtual overcomplete dictionary is constructed according to the KR product’s property. Finally, the sparse DOA estimation is solved by utilizing the idea of a sparse representation of array covariance vectors (SRACV). The simulation results demonstrate the superior performance and the low computational complexity of the proposed algorithm.

Multiple-input multiple-output (MIMO) systems require numerous analog-to-digital converters (ADCs) to support their matched filtering system, resulting in increased system cost and significant demands on transmission and storage resource. In this paper, we investigate the mixed-resolution ADCs-based structure for the bistatic MIMO radar system as a trade-off between the system performance and cost, where the entire receive branch is quantized in either low or high resolution. In bistatic MIMO radar, the direction information, including the direction of departure (DOD) and direction of arrival (DOA), is a key factor for locating the targets. Thus, the effects of exploiting mixed ADCs on the estimation accuracy are first investigated by analyzing the constructed system model. Then, the generalized Cram´er-Rao bound (CRB) expression for the proposed scheme is derived, where the multiple target scenario is considered. Compared with the traditional and one-bit bistatic MIMO radar, the proposed scheme is a more effective and flexible solution. Furthermore, the selection of low-resolution quantized receive branches affects the DOA estimation performance, while the DOD estimation performance remains unaffected due to the entire receive branch quantization method. Numerical simulations are conducted to validate the proposed scheme and derived CRB expression.

Existing methods for generating synthetic aperture radar (SAR) deception jamming signals have slow speed, low imaging quality, and insufficient intelligence in complex electromagnetic environments. This paper proposes a deep learning‐based SAR deception jamming signal generation method based on deep echo inversion Unet (DEIUnet). This method has high speed and provides high‐image quality of the interference signal. A Swin Next (SN) block is proposed to combine local and non‐local information in the image and echo data. The Unet structure consists of SN blocks, and a residual connection is used as the jump connection to fuse the multi‐scale feature information from the echo and image data. PixelShuffle is utilised for up‐sampling to generate high‐quality echo data. The experimental results on MSTAR and Sentinel‐1 data sets verify the effectiveness and superiority of DEIUnet for echo inversion. The imaging results of the SAR deception jamming signal generated by DEIUnet on an MSTAR scene confirm the effectiveness of the proposed method.