Mainlobe jamming suppression with polarimetric multi-channel radar via independent component analysis
Introduction
Electronic countermeasure (ECM) techniques are aimed at denying information (detection, position, track initiation, etc.) that the victim radar cannot work effectively [1], [2]. Mainlobe ECM, as an effective category of ECM techniques, has significantly degraded the performance of modern radar systems. It usually utilizes suppressive jamming or deceptive jamming to enter from the mainbeam of the receiving antenna so as to prevent radar from working effectively and reduce radar's reliability. Moreover, the mainlobe jamming can be generally divided into two types [3]. The first type is self-defensive jamming (SDJ), where the jammer is installed on the target. And the direction of arrival (DOA) of the jamming signal is equal to that of the target echo. The second type is escort-support jamming (ESJ), also called as near-mainlobe jamming, where the jammer is located near the target. The escort-support jamming signal enters from the mainbeam of antenna, and the DOA of the jamming signal is slightly different from the target echo.
With the rapid development of ECM, so-called electronic counter countermeasures (ECCM) techniques have been developed which are aimed at countering the effects of the enemy's ECM and eventually succeeding in the intended mission. In order to combat mainlobe ECM, several mainlobe ECCM strategies have been proposed. In general, the approaches can be categorized as two categories according to jamming types. The first category mainly is bent on the deceptive jamming (i.e., false targets) suppression. For instance, multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA) [4], [5], [6], [7], pulse agility [8], [9], distributed radar systems [10], [11], [12], [13] and coupled sequential estimation [14], [15] are investigated to resist mainlobe deceptive jamming. However, the premise of these methods is that the radar system can detect targets (real targets and false targets) successfully. When the radar is interfered by the mainlobe suppressive jamming, it is difficult to detect targets. Therefore, aforementioned approaches will be invalid in the presence of mainlobe suppressive jamming.
Accordingly, the second category is aimed at resisting suppressive jamming. For example, in [16], suppressive jamming was canceled by resorting to a modified block matrix (MBM). However, the prior information with an accurate angle of the jamming signal is necessary. In [17], an auxiliary array was devised to combat mainlobe suppressive jamming for ground-based radar. Unfortunately, it is limited in the practical applications because a large space is required to deploy the large aperture auxiliary array. In [18], a logarithm transform was proposed to suppress noise frequency modulation (NFM) jamming. However, it is able to suppress the jamming signals with constant modulus and cannot resist other suppressive jamming types. In [19], spatial polarization characteristics of antennas were exploited to suppress mainlobe blanket jamming. However, if the polarization state of jamming cannot be accurately estimated, the performance of the proposed approach will be greatly reduced. In [20], an optimized mainlobe jamming cancellation filter was designed to suppress mainlobe jamming for distributed monopulse arrays while it would be inaccurate to replace the covariance matrix of jamming-plus-noise with the covariance matrix of the received signal.
Recently, independent component analysis (ICA), also known as blind source separation (BSS), has been widely used in many fields, such as array signal processing, wireless communication, acoustic signal processing [21], [22], [23]. Meanwhile, several ICA-based methods have been proposed to deal with the problem of jamming cancellation. For example, in [24], the authors used ICA based on Gaussian moments to suppress noise suppressive jamming for the first time. In [25], [26], [27], the methods based on ICA were introduced to suppress mainlobe jamming and estimate targets' parameters. In [28], [29], ICA-based space-time multi-channel algorithm was proposed to suppress complex-jamming. However, these methods only suppress escort-support suppressive jamming and cannot combat self-defensive suppressive jamming. Currently, there is no relevant literature about applying ICA to suppress self-defensive suppressive jamming.
In this paper, we mainly propose an ICA-based method to resist self-defensive suppressive jamming by exploiting polarization characteristics. The main contributions of this paper are summarized as follows. (1) By analyzing polarization characteristics of the target and jammer, we derive a signal model involving the target and self-defensive suppressive signals for the polarimetric radar system. (2) An ICA-based method is proposed to combat self-defensive suppressive jamming. (3) We provide exhaustive numerical simulations to evaluate the performance of the proposed method.
The rest work is organized as follows. In Section 2, the signal model involving the target and jamming signals for the polarimetric radar system is introduced. In Section 3, a detailed ICA-based method is presented to combat self-defensive suppressive jamming. In Section 4, the numerical simulations are provided to evaluate the performance of the proposed method. Finally, some conclusions are summarized in Section 5.
Section snippets
Signal model
The conventional ICA-based methods can effectively suppress near-mainlobe jamming i.e., ESJ. However, when the jammer is loaded on the target, the conventional methods will suffer from the failure. In order to combat self-defensive suppressive jamming, polarization characteristics are considered. As a consequence, the signal model for the polarimetric multi-channel radar system accounting for the target and jamming signals is derived.
We consider a polarimetric multi-channel radar system as
Jamming suppression method
ICA [31], also known as BSS, is a increasingly popular statistical technique. It has served as a useful mathematical tool in recovering the unknown and statistically independent components from the observed signals.
Assume that the observed signal vector is and the source signal vector is , the classical instantaneous noisy linear ICA mixture model can be expressed as: where the is the unknown mixing matrix and constants mixing
Numerical results
In this section, we investigate the performance of the proposed method through using numerical simulations. In particular, we consider a polarimetric multi-channel radar system with receiving elements assuming an inter-element spacing . Assume that the polarization vector of the transmitted antenna is . And the polarization vectors of the receiving elements are defined as Eq. (34).
Conclusions
In this paper, exploring polarization characteristics of the target, we have proposed an ICA-based approach for polarimetric multi-channel radar to suppress mainlobe jamming. Specifically, the signal model of polarimetric multi-channel radar accounting for the target and jamming signals was derived. Then, the approach based on ICA was utilized to separate the target component and jamming component while achieving the mainlobe jamming suppression. Finally, numerical simulation results have
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61771109, 61871080 and 61701088, by ChangJiang Scholar Program, by the 111 project No. B17008, by the Fundamental Research Funds for the Central Universities under Grant 2672018ZYGX2018J016.
Mengmeng Ge received the B.S. degree in electronic information science and technology from Zhengzhou University, Zhengzhou, China, in 2016. She is currently working toward the Ph.D. degree in School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. Her research interests include radar signal processing, waveform design, and their application in radar ECCM.
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Mengmeng Ge received the B.S. degree in electronic information science and technology from Zhengzhou University, Zhengzhou, China, in 2016. She is currently working toward the Ph.D. degree in School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. Her research interests include radar signal processing, waveform design, and their application in radar ECCM.
Guolong Cui received the B.S., M.S., and Ph.D. degrees from University of Electronic Science and Technology of China (UESTC), Chengdu, in 2005, 2008, and 2012, respectively. From January 2011 to April 2011, he was a visiting researcher with University of Naples Federico II, Naples, Italy. From June 2012 to August 2013, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ. From September 2013 to July 2018, he was an Associate Professor in UESTC. Since August 2018, he has been a Professor in UESTC. His current research interests include cognitive radar, array signal processing, MIMO radar and through the wall radar.
Xianxiang Yu received the B.S. and Ph.D. degrees from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2014 and 2020, respectively. From October 2018 to September 2019, he was a Visiting Researcher with Pennsylvania State University, State College, PA, USA. He is a Post-Doctoral Fellow with UESTC. His research interests are in MIMO radar, array signal processing, and waveform optimization.
Lingjiang Kong was born in 1974. He received the B.S., M.S., and Ph.D. degrees from the University of Electronic Science and Technology of China (UESTC) in 1997, 2000 and 2003, respectively. From September 2009 to March 2010, he was a visiting researcher with the University of Florida. He is currently a professor with the School of Information and Communication Engineering, University of Electronic Science and Technology of China (UESTC). His research interests include multiple-input multiple-output (MIMO) radar, through the wall radar, and statistical signal processing.