Radar Main-Lobe Jamming Cancellation via Virtual Array Optimization Inspired Waveform Design

In this paper, we propose a target detection and localization method on distributed monopulse arrays for tracking radar. An optimized mainlobe jamming (MLJ) cancellation filter was designed by maximizing the power ratio of the received siackgnal to the jamming-plus-noise. By exploiting the different correlation characteristics between the target echo and MLJ on distributed antennas, the designed filter is able to cancel MLJ and maintain the target echo. By applying the identical filter on sum-difference beams, MLJ can be cancelled, and the monopulse ratio can be maintained simultaneously. Hence, we simply detect and locate the target on the filtering output of sum-difference beams according to the monopulse principle. Monte Carlo simulations demonstrated that the proposed filter outperforms the conventional algorithms.

It is well‐known that the noise jamming impedes the mission effectiveness of radar systems. It raises the level of the background and swamps out target returns. Techniques to suppress jamming and improve the performance of radar systems are continuously being developed. Here, a jammer being close to a target or even equivalent to it in direction, colloquially referred to as mainlobe jamming is considered. A mainlobe jamming cancellation technique based on multi‐static radar systems (MSRSs) is developed. In a MSRS, a jammer will produce different time delays and relative speeds to the diverse radar receivers located at separated positions. Here, the method to simultaneously estimate the shifts of time delay and Doppler frequency in the range‐Doppler plane (RDP) is proposed. Furthermore, the jamming cancellation is adaptively performed in the range‐Doppler domain. The simulation results show the validity of the proposed method.

In the modern complex electromagnetic environment, array radar systems usually apply adaptive beamforming techniques to form nulls toward the jamming angles for jamming cancellation. However, for mainlobe jamming, the nulls lead to beam distortion, which degrades the performance of angle measurement dramatically. This paper proposes an angle measurement method based on array partition for distributed array radars in the presence of mainlobe jamming. First, the distributed array is partitioned into a subarray-level large aperture virtual array and an element-level small aperture array. Then the adaptive beamforming is performed for the large virtual array to generate narrow nulls at the jamming direction, while the small aperture array forms monopulse sum and difference beams to measure the target angle. Thus, the proposed method effectively suppresses the mainlobe jamming and measures the target angle accurately. Finally, the correctness and effectiveness of the proposed method are verified by simulation experiments.

Radar mainlobe jamming has attracted considerable attention in the field of electronic countermeasures. When the direction of arrival (DOA) of jamming is close to that of the target, the conventional antijamming methods are ineffective. Generally, mainlobe antijamming method based on blind source separation (BSS) can deteriorate the target direction estimation. Thus in this paper, a high precision sparse reconstruction scheme for multiple radar mainlobe jammings is proposed that does not suffer from failure or performance degradation inherent in the traditional method. First, the mainlobe jamming signal and desired signal components are extracted by using the joint approximation diagonalization of eigenmatrices (JADE) method. Then, oblique projection with sparse Bayesian learning (OP-SBL) method is employed to reconstruct the target with high precision. The proposed method is capable of suppressing at most three radar mainlobe jammers adaptively and also obtain DOA estimation error less than 0.1°. Simulation and experimental results confirm the effectiveness of the proposed method.

The monopoulse technique for target angle estimation fails when there is sidelobe and/or mainlobe jamming. If not effectively encountered, electronic jamming prevents successful radar target detection and tracking. We have developed a technique which cancels one mainlobe jammer and multiple sidelobe jammers in such a way that enables both target detection and unbiased monopulse angle estimation. Our technique makes use of adaptive digital beamforming for sidelobe jamming cancellation followed by a mainlobe canceller for mainlobe jamming cancellation. A constrained adaptation is imposed to maintain the mainbeam in the sidelobe cancellation process.

Mainlobe cancellation refers to an advanced technique with the salient feature of maintaining the monopulse angle estimation accuracy of the target while canceling a mainlobe jammer. This technique makes use of the high-gain monopulse beams including the delta-delta beam for jamming cancellation. The technique is based on an orthogonal nulling concept where jamming is canceled in one angular domain while monopulse processing is carried out in another angular domain. This salient feature can be described in terms of product patterns where the common adapted patterns are canceled out leaving the monopulse ratios un-distorted. In this paper, we present some extensions of this technique for coping with multiple mainlobe and/or sidelobe jamming using the digital beamforming capabilities. From the perspective of array adaptivity, these extensions can be considered to be partial adaptive techniques designed for rectangular aperture with separable jamming cancellation and monopulse processing.

In modern electronic warfare, array radar systems apply adaptive beamforming techniques to form nulls in the beams toward the jamming angles for multiple jamming cancellation. However, the nulls lead to distortion, especially within the mainlobe, which severely degrades the target detection ability. To address the degradation, this letter proposes joint adaptive beamforming techniques for distributed array radars by two-stage adaptive processing. First, the linearly constrained minimum variance beamforming is performed for multiple sidelobe jamming cancellation with mainlobe maintenance within each array radar. After that, joint beamspace processing with multiple radars is carried out for multiple mainlobe jamming cancellation using adaptive–adaptive algorithm. Excellent performance in both jamming cancellation and target detection is attained at very low computation and data transmission cost between multiple radar sites. Detailed evaluation and simulation results are provided to validate the proposed technique.

The main contribution of this thesis is to study the signal processing issues in MIMO radar and propose novel algorithms for improving the MIMO radar system. In the first part of this thesis, we focus on the MIMO radar receiver algorithms. We first study the robustness of the beamformer used in MIMO radar receiver. It is known that the adaptive beamformer is very sensitive to the DOA (direction-of-arrival) mismatch. In MIMO radar, the aperture of the virtual array can be much larger than the physical receiving array in the SIMO radar. This makes the performance of the beamformer more sensitive to the DOA errors in the MIMO radar case. In this thesis, we propose an adaptive beamformer that is robust against the DOA mismatch. This method imposes constraints such that the magnitude responses of two angles exceed unity. Then a diagonal loading method is used to force the magnitude responses at the arrival angles between these two angles to exceed unity. Therefore the proposed method can always force the gains at a desired interval of angles to exceed a constant level while suppressing the interferences and noise. A closed form solution to the proposed minimization problem is introduced, and the diagonal loading factor can be computed systematically by a proposed algorithm. Numerical examples show that this method has an excellent SINR (signal to noise-plus-interference ratio) performance and a complexity comparable to the standard adaptive beamformer. We also study the space-time adaptive processing (STAP) for MIMO radar systems. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (phased array radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this thesis, we explore the clutter space and its rank in the MIMO radar. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). Using this representation, a new STAP algorithm is developed. It computes the clutter space using the PSWF and utilizes the block diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity. The second half of the thesis focuses on the transmitted waveform design for MIMO radar systems. We first study the ambiguity function of the MIMO radar and the corresponding waveform design methods. In traditional (SIMO) radars, the ambiguity function of the transmitted pulse characterizes the compromise between range and Doppler resolutions. It is a major tool for studying and analyzing radar signals. The idea of ambiguity function has recently been extended to the case of MIMO radar. In this thesis, we derive several mathematical properties of the MIMO radar ambiguity function. These properties provide some insights into the MIMO radar waveform design. We also propose a new algorithm for designing the orthogonal frequency-hopping waveforms. This algorithm reduces the sidelobes in the corresponding MIMO radar ambiguity function and makes the energy of the ambiguity function spread evenly in the range and angular dimensions. Therefore the resolution of the MIMO radar system can be improved. In addition to designing the waveform for increasing the system resolution, we also consider the joint optimization of waveforms and receiving filters in the MIMO radar for the case of extended target in clutter. An extended target can be viewed as a collection of infinite number of point targets. The reflected waveform from a point target is just a delayed and scaled version of the transmitted waveform. However, the reflected waveform from an extended target is a convolved version of the transmitted waveform with a target spreading function. A novel iterative algorithm is proposed to optimize the waveforms and receiving filters such that the detection performance can be maximized. The corresponding iterative algorithms are also developed for the case where only the statistics or the uncertainty set of the target impulse response is available. These algorithms guarantee that the SINR performance improves in each iteration step. The numerical results show that the proposed iterative algorithms converge faster and also have significant better SINR performances than previously reported algorithms. (Abstract shortened by UMI.)

Conventional adaptive monopulse can adequately estimate the target angle in the case of sidelobe jamming where two adaptive sum and difference beams are specifically employed with spatial nulling in the direction of interference and nearly no distortion of mainlobe pattern. However, when there exist jamming signals impinging upon the mainlobe of receiving an­tennas, conventional adaptive monopulse can suppress mainlobe jamming by the technique of spatial filtering, but in the meantime it brings about a distorted mainlobe pattern that hampers accurate angle estimation. Multistatic radar has the capability of mainlobe jamming cancellation. In this paper, a scheme of adaptive monopulse estimation in mainlobe jamming for multistatic radar is proposed. The angle estimation performance is accordingly evaluated. Simulation results demonstrate that the proposed monopulse processor that uses multistatic radar to mitigate mainlobe interference can improve the angle estimation accuracy over conventional adaptive monopulse.

Mainlobe jamming suppression with polarimetric multi-channel radar via independent component analysis

A MIMO radar can separate the transmission paths to the receiver and then increase the number of transmission paths. In MIMO radar, the added path channels are similar to virtual array elements. The optimization of the arrangement position of the virtual array elements enables the MIMO radar to obtain optimized beam performance. The difference between MIMO radar array structure optimization and traditional phased array radar array structure optimization is that MIMO radar needs to optimize the virtual array beam by optimizing the location of the actual transceiver array. For a uniform line array with co-located transceivers, the virtual array elements will generate a lot of redundancy, and through the non-uniform arrangement of the actual array elements, the virtual array element aperture is expanded and the redundancy will be greatly reduced. Aiming at the optimization problem of MIMO radar array structure, this paper studies the optimization method of MIMO radar array structure by space-time autoregressive algorithm with the lowest cost function of virtual array beam ratio. In this paper, the simulation experiment of algorithm optimization proves that when there is interference, the algorithm optimization can improve the primary and secondary ratio of the array beam, and it also shows that the algorithm has good performance.

This paper presents an optimum linear array geometry for 2q-th order cumulant-based array processing for highresolution direction of arrival (DOA) estimation. The concept of Khatri-Rao (KR) product gives the difference co-array which makes larger virtual array aperture. The virtual array geometry depends on the original physical array configuration, and the minimum redundancy array (MRA) can be regarded as one of the optimum arrays in the case of q = 1. The 2q-level nested array is an efficient array in the case of q ≥ 2 but it does not become an optimum geometry with the maximum degree-of-freedom (DOF). In this paper, we try to extend the MRA to have more DOF in comparison with 2q-level nested array. We use the 2q-MUSIC algorithm for DOA estimation, and the accuracy of the proposed array becomes better than that of 2q level nested array. The performance of the proposed array is evaluated through computer simulation.

Airborne radars are susceptible to a large number of clutter, noise and variable jamming signals in the real environment, especially when faced with active main lobe jamming, as the waveform shortcut technology in the traditional regime can no longer meet the actual battlefield radar anti-jamming requirements. Therefore, it is necessary to study anti-main-lobe jamming techniques for airborne radars in complex environments to improve their battlefield survivability. In this paper, we propose an airborne radar waveform design method based on a deep reinforcement learning (DRL) algorithm under clutter and jamming conditions, after previous research on reinforcement-learning (RL)-based airborne radar anti-jamming waveform design methods that have improved the anti-jamming performance of airborne radars. The method uses a Markov decision process (MDP) to describe the complex operating environment of airborne radars, calculates the value of the radar anti-jamming waveform strategy under various jamming states using deep neural networks and designs the optimal anti-jamming waveform strategy for airborne radars based on the duelling double deep Q network (D3QN) algorithm. In addition, the method uses an iterative transformation method (ITM) to generate the time domain signals of the optimal waveform strategy. Simulation results show that the airborne radar waveform designed based on the deep reinforcement learning algorithm proposed in this paper improves the signal-to-jamming plus noise ratio (SJNR) by 2.08 dB and 3.03 dB, and target detection probability by 26.79% and 44.25%, respectively, compared with the waveform designed based on the reinforcement learning algorithm and the conventional linear frequency modulation (LFM) signal at a radar transmit power of 5 W. The airborne radar waveform design method proposed in this paper helps airborne radars to enhance anti-jamming performance in complex environments while further improving target detection performance.

When the training data contains target signals and the target steering vector mismatch for multiple-input multiple-output with frequency diverse array (FDA-MIMO) radar, the performance of mainlobe jamming suppression will be sharply declined. Considering that the nonlinear FDA-MIMO radar has no grating lobes in the range dimension, an exponential FDA-MIMO radar jamming suppression method combining diagonal loading and eigenvector elimination (DL-EVE) is proposed, which make full use of the correlation between the target steering vector and the signal subspace. Simulations show that under the conditions of low snapshots and low input signal-to-noise ratio (SNR), compared eigenspace based beamformer (ESB) algorithm, the proposed method can still effectively suppress the mainlobe jamming and output a higher signal-to-jamming and noise ratio (SJNR).

Radar is known as the “eye” of modern warfare and plays a pivotal role in warfare, but the detection performance of radar systems is seriously affected by main-lobe cover jamming. To solve this problem, a radar target detection method based on four-channel monopulse radar is proposed in this paper. This method designed a spatial filter for main-lobe jamming cancellation, which could cancel the main-lobe jamming while keeping the target signal power unchanged, ensuring that the Electronic counter-countermeasure Improvement Factors (EIF) of the filtered signal always reach the maximum value to improve the target detection performance of monopulse radar. The Monte Carlo simulation results showed that the algorithm in this paper outperformed the conventional Main-Lobe Cancellation (MLC) algorithm in terms of EIFs and detection performance.