For nonside-looking airborne radar (NSLAR), the range-dependent near-range clutter degrades the performance of space–time adaptive processing (STAP), especially in the high-pulse-repetition-frequency mode. To solve this problem, a novel algorithm using sparse recovery (SR) and orthogonal projection (OP) is proposed in this letter. First, the elevation angle of short-range clutter is estimated by the off-grid SR method. Second, the near-range clutter is suppressed in the elevation domain by the OP method. Finally, the azimuth–Doppler STAP is carried out to suppress the residual range-independent far-range clutter. The proposed algorithm can obtain the elevation angle of short-range clutter more precisely with only one training snapshot and it is robust since no prior knowledge is needed. The simulation results validate the effectiveness of the proposed algorithm.

Range-dependent clutter suppression is a challenging problem in nonsidelooking, bistatic, and conformal antenna array airborne radar, especially in the presence of range ambiguity. Superimposed stepped frequency (SSF) radar employs a small frequency increment across the stepped signals in each pulse, which introduces a dimension related to slant range. In this article, an airborne radar framework is established, which takes SSF signal as the transmit waveform. Thus, a two-stage adaptive clutter suppression method is proposed, which utilizes the degrees of freedom in range, space, and time domains provided by airborne SSF radar. In the first stage, the secondary range dependence compensation approach is adopted to distinguish the clutter of each range region in the carrier frequency domain. Then, a target-free covariance matrix is estimated by the compensated data, and the covariance matrix is used for the range-ambiguous clutter separation in the carrier frequency domain. Thus, the space-time snapshot of each range region can be extracted. In the second stage, a clutter segmentation processing method is devised for residual clutter suppression. For the near-range unambiguous clutter, the traditional clutter compensation approach is applied to further align the spectrum distribution of clutter, while the conventional space-time adaptive processing is utilized to suppress far-range clutter directly. Since the proposed method takes full advantage of the range dimension and the two stages of adaptive processing are data-dependent, excellent clutter suppression performance can be obtained. The proposed method can also extract the real range of the target since each range region is processed separately. Simulation results demonstrate the effectiveness of the proposed method.

By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary beams, leading to greater demands on the training samples and heavier computational burdens than the conventional STAP methods. To solve this problem, the ideas of sum–difference beams, generalized multiple beams and Doppler-domain localization are applied here, and three improved dimension-reduced structures of 3D-STAP are proposed in this article. After analyzing the characteristics and distribution of nonstationary clutter for SBEWR, we find that the demands for auxiliary beams are different in elevation, azimuth and Doppler dimension. In addition, the suggestion to choose the number of auxiliary beams in each dimension is given. Simulation experiments are conducted to verify the analysis and evaluate the performance of the proposed methods. The simulation results show that the proposed 3D-STAP methods have better performance and lower computational burdens than typical 3D-STAP methods.

Due to the range ambiguity effect and the complex terrain environment, the remote weak target of interest for non-sidelooking airborne radar is usually superimposed with the nonstationary and heterogeneous short-range strong clutter, so it is difficult for the traditional space-time adaptive processing (STAP) methods to achieve effective moving target detection. Therefore, the detection of the remote weak moving target in the heterogeneous and nonstationary clutter environment is one of the difficulties encountered by airborne radar. Through the analysis of airborne radar clutter characteristics, we have found that the short-range nonstationary clutter and the long-range ambiguous clutter do not overlap in Doppler domain, so the homogeneous training samples can be effectively selected through the digital terrain database. On this basis, the paper establishes the refined grid dot level clutter signal model of airborne radar and proposes a 3D-STAP method based on the digital terrain database, namely the DTD-3D-STAP method. The method first accurately registers the airborne radar echo with the grid dots of the digital terrain database; next, the training samples are selected along the equal Doppler line based on prior knowledge; then the training samples can be compensated through power compensation; and finally, the short-range nonstationary clutter is suppressed through 3D-STAP technology. On the one hand, the proposed method ensures the homogeneity of the clutter spectrum and power of the training samples, and the sample can be detected through prior knowledge and power compensation. On the other hand, 3D-STAP technology is used to effectively suppress nonstationary clutter. Computer simulation and experimental results verify the effectiveness of the proposed method.

Owing to the Earth's rotation and the rapid motion of satellites, the clutter received by space-based radar (SBR) exhibits severe non-stationary characteristics, notably evidenced by the Doppler frequency of clutter varying with range, particularly in the near-range region. Elevation prefiltering is shown to be an effective algorithm for suppressing near-range non-stationary clutter. Based on the characteristics of SBR with several near-range bending clutter, this article proposes an elevation prefiltering algorithm based on presequence pulse echoes to suppress non-stationary clutter for SBR. The training samples selected only contain near-range non-stationary clutter, which is obtained from the elevation dimension data of the presequence pulse echoes. By excluding the influence of far-range clutter, the proposed algorithm demonstrates superior performance in suppressing near-range clutter. After attenuating the near-range non-stationary clutter, the azimuth-Doppler space-time adaptive processing (STAP) algorithm is cascaded to further suppress far-range stationary clutter. Simulation results show the effectiveness in suppressing near-range clutter and enhancing target detection capabilities for SBR of the proposed method.

Compared with ground-based radar, airborne radar has excellent low-altitude moving target detection performance. With the development of economics, ground radiation sources are widely distributed, and the low-frequency spectrum is extremely crowded. When airborne radar operates over land, dense unintentional jamming can have a severe impact on target detection. The effects are mainly reflected in the following three aspects: 1. It is easy for jamming to enter from the mainlobe, and the mainlobe jamming is formed, which is difficult to be suppressed by traditional anti-jamming methods. 2. Since the jamming distribution is dense, the degrees of freedom (DOFs) of jamming exceed the spatial DOFs of the system. 3. Jamming and clutter exist simultaneously, strong clutter is difficult to be suppressed effectively. In this article, a subband space–time adaptive processing method for airborne radar based on the sparse frequency waveform is proposed. First, the jamming frequency and azimuth angle are obtained in passive mode, and accordingly, the sparse frequency waveform is designed to suppress mainlobe jamming. Then, the echo data are divided into subbands, and each subband is processed by subband sidelobe cancellation processing to achieve the suppression of dense sidelobe jamming. Finally, the strong clutter is suppressed by space-time adaptive processing. The simulation results demonstrate that the proposed method has excellent performance in suppressing dense unintentional jamming and clutter.