Unlike the conventional phased array, the frequency-diverse array (FDA) employs a tiny frequency increment across the array elements, which is capable of providing range–angle–time-dependent beampattern, and thereby offers potential benefits in target localization and interference mitigation. Reported literature on FDA mostly focuses on its intrapulse (fast-time) property and ignores the interpulse (slow-time) feature induced by frequency offset. In this article, incorporating the neglected inherent slow-time coding feature, a novel slow-time FDA multiple-input multiple-output (FDA-MIMO) technique is proposed for the space-time adaptive processing (STAP) radar. The new coding scheme can separate transmitting (Tx) signals via slow-time Doppler filtering, and the problem of signal aliasing is tackled by appropriately designing the slow-time codes. At the receiving (Rx) side, two Rx schemes are devised for recovering range-dependent Tx degrees-of-freedom. Moreover, the relevant clutter rank and the rule for code design are derived in detail. The outstanding merits of the proposed slow-time FDA-MIMO STAP radar consist of the following: There is no specific restriction on the probing waveform; the negative effect of time-variant pattern is perfectly eliminated; and both the target localization accuracy and clutter suppression performance are superior over the state-of-the-art FDA radar systems. Numerical results corroborate the superiorities of the proposed waveform strategy for STAP application.

Polarization sensitive frequency and FDA-MIMO hybrid (PSFDA-MIMO) radar is an emerging technology. The existing algorithms for parameter estimation with PSFDA-MIMO radar need multiple-dimensional searches, whose computational complexity is high. To reduce the computational complexity, in this paper a search-free algorithm is proposed to estimate four-dimensional parameters with PSFDA-MIMO radar. First, direction of arrival (DOA) is estimated with the ESPRIT algorithm using the rotational invariance of the receiving array. Second, based on the estimated DOA parameter, range estimation is obtained with the ESPRIT algorithm using the rotational invariance of the transmitting array. Finally, polarization parameters are estimated with the ESPRIT algorithm using the rotational invariance of the polarization domain. Thus, the proposed algorithm is termed the successive ESPRIT algorithm. Simulation results demonstrate the effectiveness of the proposed algorithm.

This paper studies the problem of 2-D direction of arrival (DOA) estimation with polarization sensitive array (PSA), in which polarized vector sensor is consisted of spatially spread crossed-dipole (SSCD). It is well known that crossed-dipole vector sensor can obtain all the polarization information including HH, HV, VH, and VV. Consequently, it is one of the most popularly polarized vector sensor. Unfortunately, in the existing works, it is difficult to effectively solve the problem of DOA estimation with SSCD array. Motivated by this, this paper presents a sparse rectangular SSCD-PSA to solve this problem. First, the ESPRIT algorithm is adopted to calculate the high accuracy but ambiguous direction cosine estimation. Second, the polarization information in the data covariance matrix is incorporated into incident source to form a block sparse signal model, and then, the block orthogonal matching pursuit is used to get a coarse reference direction cosine estimation. At last, a finally fine 2-D DOA estimation can be obtained by disambiguation process using the combination of high accuracy but ambiguous and coarse reference direction cosine estimations. The proposed algorithm obtains the 2-D DOA estimation under the lowest components polarized vector sensor and keeps the advantage of low mutual coupling. The simulation results prove the effectiveness and correctness of the proposed algorithm.

In bistatic multiple input multiple output (MIMO) radar, more number of detectable incident signals and a higher angle estimation performance can be obtained by using conjugate estimation of signal parameters via rotational invariance techniques (ESPRITs) with the characteristic of noncircularity. The result is achieved under the assumption that all the received signals are noncircular. When the incident signals are the coexistence of noncircular and circular signals, the conjugate ESPRIT will not valuable. Therefore, this paper proposes a method of the joint of direction of departure (DOD) and direction of arrival (DOA) estimation, which is appropriate for the coexistence of noncircular and circular signals in bistatic MIMO radar. First, the received data model of the bistatic MIMO radar is given. Second, we modify the received signal model by the use of noncircularity characteristic. Third, we derive out the equation of spatially rotational invariant containing the DOD and DOA information. Last, we solve the equation to obtain the DOD and DOA by means of total least squares technology. The proposed algorithm has the three advantages. One is that it has better angle estimation accuracy than that of method which does not use the noncircularity characteristic. Another one is that it has more number of detectable incident signals. The last one is that the more number of noncircular signals, the higher angle estimation accuracy will be. Results from numerical experiments are used to show the effectiveness of our proposed algorithm.

In this work, we consider the problem of joint sparse array antenna design and phase-only beampattern shaping with reconfigurability. Our design objective is to reduce the cost of the antenna system for the unmanned aerial vehicle (UAV) radar with a large-aperture array. We formulate this co-design problem as a sparse optimization problem with angular response and binary-like modulus constraints. To solve the resultant NP-hard nonconvex problem, we propose a consensus alternating direction method of multipliers (ADMM) based algorithm. This method transforms the original problem into a higher-dimensional one by introducing two categories of auxiliary variables. By doing so, the problem can be effectively tackled via alternately solving several subproblems, meanwhile the tricky binary modulus constraints can be well handled by utilizing the geometric property of the subproblem. We further prove that the proposed algorithm converges to the KKT points of the original problem. In addition, we extend our methodology to the discrete phase case to handle the practical finite-bit phase quantization. The devised algorithm is capable of generating phase-only weight vector meanwhile automatically selecting appropriate antenna elements according to the specific beam shape. Numerical examples are given to demonstrate the effectiveness of the proposed approach.

The result of radar signal sorting directly affects the performance of electronic reconnaissance equipment. Sorting method based on intra-pulse features has become a research focus in recent years. However, as the number of extracted features increases, the dimension of the feature vector becomes higher and higher, which makes the complexity of the sorting algorithm increase geometrically. In this way, feature selection becomes more and more necessary. Combining the latest research on fuzzy rough sets, this paper proposes two feature selection methods, namely two-steps attribute reduction based on fuzzy dependency (TARFD) algorithm and fuzzy rough artificial bee colony (FRABC) algorithm. The TARFD method uses the candidate attribute set as starting point, according to the definition of the redundant attribute set. Then the less important attributes are successively eliminated. The FRABC method starts from the dependence degree of fuzzy rough set, and constructs a fitness function that reflects the importance of the attribute subset and the reduction rate. Based on this function, the artificial bee colony algorithm is used to reduce the attributes of the dataset. Using the TARFD and FRABC algorithms, the extracted feature sets, including entropy feature set, Zernike moment feature set, pseudo Zernike feature set, gray level co-occurrence matrix (GLCM) feature set, and Hu-invariant moment feature set are processed, then an optimal feature subset was obtained and a sorting test was performed. The results show the effectiveness of the extracted intra-pulse features and the efficiency of the feature selection algorithm.