In this letter, the clutter rank estimation formulas for end-fire array airborne radar (EFAAR) in two typical scenarios are derived. In comparison to the airborne radar with a traditional broadside array, the clutter rank of the EFAAR in the forward-looking scenario still maintains a reasonably accurate estimation criterion. The effectiveness of the proposed clutter rank formulas is validated, and the impacts of certain parameters on the clutter rank are analyzed through simulations. Simulation results indicate a direct proportionality between clutter rank and the number of array elements, coherent processing pulses, and clutter ridge slope.

Multiple-input multiple-output (MIMO) radar can improve target detection capability due to its increased spatial degrees of freedom compared with traditional phased array radar. However, in practical applications, perfectly orthogonal waveforms are virtually unattainable. Thus, the orthogonality among the transmitted waveforms becomes a critical factor for determining the operational state in orthogonal MIMO radar. In typical target detection scenarios characterized by complex environments, the received radar echoes are often superimposed by reflections of multiple distinct objects. Such echo signals inevitably impact the orthogonality performance of non-orthogonal waveforms, thereby affecting the detection performance of the MIMO radar. To address this, the article initially undertakes a rigorous analysis of echo signals for non-orthogonal waveforms under both single-target and multi-target conditions. Subsequently, based on the analytical findings, we propose an objective function for the design of constant modulus code division multiple access waveforms tailored for multi-target scenarios. This objective function is then employed as a loss function in an unsupervised training scheme using a recurrent neural network with a long short-term memory architecture. Furthermore, we utilize a nonlinear function to discretize the network output, making the output phase coding approximate to binary coding. This makes the resulting phase coding more practically applicable. Finally, normalization is performed on the output amplitude of each matched filter to further enhance waveform orthogonality. Simulation results are provided to demonstrate the performance of the proposed method.

The range ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous range occupies most of the Doppler spectrum, which leads to the degradation of the traditional space-time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output can provide the ability to identify the range ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this paper, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range ambiguous clutter, is proposed. Firstly, EPC is used to pre-whiten the most range ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method.

Forward-looking mode for moving target detection is important for airborne radar systems, however, it is difficult to suppress the range-dependent clutter in the presence of range ambiguity using the traditional space-time adaptive processing (STAP) techniques. In this paper, a vertical coherent frequency diverse array (FDA) radar using quadratic phase coding (QPC) is proposed to alleviate the range-ambiguous clutter problem. The proposed vertical coherent FDA radar has two major advantages: i) it uses an identical baseband waveform for each transmit element, which prevents the unreal orthogonal waveform assumption in multiple-input multiple-output (MIMO) framework; ii) it achieves wide spatial coverage in elevation within a single pulse duration, which is desirable for the airborne reconnaissance radar. The space-frequency coupled characteristic of vertical coherent FDA is revealed and a series of 2-dimensional (2-D) matched filters are designed, which is helpful for range-ambiguous clutter separation. Based on the QPC technique, the residual clutter is suppressed by orthogonal projection (OP) filtering, which further improves the target detection performance. With the proposed vertical coherent FDA using QPC, the range-ambiguous clutter can be separated successfully, and the clutter suppression performance is improved. Simulation results are presented to verify the effectiveness of the proposed method in serious range-ambiguous clutter scenarios.

The space–time adaptive processing (STAP) technique has achieved good clutter suppression performance for a side-looking airborne radar; however, it suffers from severe performance degradation in nonside-looking airborne radar. This is because the clutter distribution varies considerably with range. The 3D STAP can provide better performance compared with the traditional STAP methods in such a nonstationary clutter environment, but it requires high computational complexity and sample support. In this letter, the characteristic of azimuth–elevation–Doppler 3-D beam pattern for the planar array is explored, and a novel reduced-dimension scheme combined with this characteristic is proposed. We further developed three basic reduced-dimension structures according to this scheme. Furthermore, we also prove that the first and third structures are just the direct extension of the traditional 2-D generalized multibeam and joint-domain localized methods. The second one is an original structure that performs local reduced-dimension processing with three orthogonal planar windows. Numerical results verify that the proposed structures have significant advantages in reducing computational load and training sample numbers compared with existing 3D STAP methods. In particular, the second structure shows the best comprehensive performance in the bad samples environment.

Sparse clutter nulling space–time adaptive processing (STAP) methods achieve superior performance in the case of limited samples, so they are suitable for nonhomogeneous clutter environments. Traditional sparse clutter nulling STAP algorithms try to estimate the clutter subspace by selecting a suitable set of space–time steering vectors (atoms) in spatial-Doppler profile grids. However, the off-grid effect is inevitable for the nonside-looking case due to the nonlinear distribution of clutter and, thus, leads to significant performance degradation. To solve this problem, a gridless sparse clutter nulling STAP algorithm based on the particle swarm optimization (PSO) named PSO-STAP is proposed in this letter. A criterion function to evaluate the suitability of atoms is first devised. By regarding the criterion as a fitness function, PSO-STAP can select atoms in gridless spatial-Doppler profile, which is more suitable to construct the accurate clutter subspace. Numerical results verified the feasibility and superiority of the proposed algorithm.

The ultrahigh frequency ultra-wideband synthetic aperture radar (UHF UWB SAR) has the well foliage penetrating and high-resolution imaging, which can be used to detect the concealed area under the foliage in forests. This paper presents an airborne UHF UWB SAR experiment and imaging results. During the winter, an airborne campaign has been carried out in Shanxi Province in China, and the raw data was collected. In this experiment, the SAR system was integrated onboard a CESSNA-172 airplane. The antenna was fixed on the suspension arm of the right wing of the airplane, while the other part of the SAR system was placed on the back seat of this airplane. The experimental results have been obtained from the collected raw data, which proved the imaging performance of the airborne UHF UWB SAR system as well as the validity of the imaging method.

As training samples are not always target-free in heterogeneous environments, the generalized inner product (GIP) method is usually used to censor the training samples contaminated by targetlike signals (outliers). However, the GIP method incurs significant performance degradation when there are multiple outliers in the original training sample set. To deal with this problem, this letter proposes a novel GIP method. First, the principal component, which results in performance degradation of the GIP method, is obtained via extracting the maximum of cross-spectral metric (CSM) between the target steering vector and the eigenspace of the GIP's test covariance matrix (TCM). Second, taking a sample covariance matrix (SCM) as the initial TCM, a new TCM is reconstructed by setting the eigenvalue of SCM that corresponds to the largest CSM between the target steering vector and the SCM's eigenspace to be noise variance. Finally, the new TCM is combined with the conventional GIP method to form a novel GIP statistic to eliminate the contaminated training samples. Numerical results with both simulated and mountain-top data confirm the improvement of the proposed method.

To improve the performance of the recently developed parameter‐dependent sparse recovery (SR) space–time adaptive processing (STAP) algorithms in real‐world applications, the authors propose a novel clutter suppression algorithm with multiple measurement vectors (MMVs) using sparse Bayesian learning (SBL) strategy. First, the necessary and sufficient condition for uniqueness of sparse solutions to the SR STAP with MMV is derived. Then the SBL STAP algorithm in MMV case is introduced, and the process for hyperparameters estimation via expectation maximisation is given. Finally, a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithm are conducted. Results with both simulated and the Mountain‐Top data demonstrate the fast convergence and good performance of the proposed algorithm.

For the problem of detecting a subspace signal in the presence of signal mismatch, we propose a two-stage detector, referred to as the AESD. The AESD is constructed by cascading two detectors, namely, the adaptive energy detector (AED) and the adaptive subspace detector (ASD). The AESD offers great flexibility in controlling the detection performance for mismatched subspace signals, since the AED is very robust, while the ASD is very selective. We derive the analytical expressions for the probabilities of detection and false alarm, which are verified by Monte Carlo simulations.

Subspace space-time adaptive processing (STAP) algorithms are able to eliminate clutter completely. However, when the number of training samples is smaller than the clutter rank, the performances of subspace STAP algorithms degrade severely due to the inaccurate estimate of clutter subspace. To remedy this problem, a novel subspace STAP algorithm is proposed. In the proposed algorithm, the entire clutter subspace is constructed by two portions. The direct portion is estimated by a conventional method from limited training samples, while the supplemented portion is constructed by some space-time steering vectors selected from an overcomplete space-time steering dictionary. Clutter suppression is achieved by projecting the data into the subspace orthogonal to the clutter subspace. Numerical results with both simulated and mountain-top data demonstrate that the proposed algorithm has superior performance in a finite-training-sample situation.