Interference cancellation in co‐located MIMO radars using waveform optimisation in signal dependent clutter

Multiple input multiple output (MIMO) radars have been intensively studied for their superior performance merits compared to conventional radars including improved spatial resolution and parameter estimation. To achieve these merits, the transmitted waveforms from the MIMO transmitting antennas should be mutually orthogonal. Herein, the orthogonality is attained in the Doppler domain, namely Doppler division multiple access (DDMA), as it enjoys the simplest structure among other orthogonality schemes. Heretofore, the research in MIMO radars has focused on optimizing MIMO radar performance from different aspects including efficient waveform design, antenna beam-forming, and adaptive processing techniques. However, MIMO radars have been also a target of deliberate jamming scenarios. In this paper, the focus is turned to the evaluation of the detection performance of DDMA MIMO radars in jamming environments by creating a two-sided engagement scenario. On the jammer side, a jamming technique is proposed, Doppler-range block jamming (DRBJ), to deny the detection of a DDMA MIMO radar equipped with a moving target detector (MTD). On the radar side, a comparative detection performance analysis of two different MIMO radar detectors, normalized adaptive matched filter (NAMF) and fast 3D constant false alarm rate (CFAR), is conducted using Monte Carlo simulations. This analysis unveils divergent design philosophies of the two detectors, with each prioritizing distinct performance objectives.

This study investigates the problem of low probability of intercept (LPI)-based distributed multiple-input multiple-output (MIMO) radar waveform design against barrage jamming in signal-dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal-dependent clutter, barrage jamming signals and coloured noise, the LPI-based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI-based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

A multiple input multiple output (MIMO) radar is an emerging field which has attracted many researchers recently. In this type of radar, unlike an standard phased array radar, one can choose transmitted probing signals freely to maximize the power around the locations of the targets of interest, or more generally to approximate a given transmit beam-pattern, or to minimize the cross-correlation of the signals reflected back to the radar by the targets of interest. These signals can be correlated to each other or noncorrelated. Many papers have investigated beampattern design and waveforming in MIMO radars. One of the most famous approaches for beampattern design is named covariance based method which design cross-correlation matrix of transmitted signals instead of the signals. On the other hand, many papers have investigated beamforming of an standard phased array radar in different ways. In this paper it is desirable to design beampattern of a phased array radar by means of the MIMO radar covariance based beampattern design method, as a special case of general MIMO radar. Although many approaches are available for beamforming of phased array radar this approach would have less complexity compare to them. MATLAB software is being employed for simulations. (6 pages)

In this paper, we deal with the problem of simultaneously detecting and tracking multiple targets using polarimetric multiple input multiple output (MIMO) radars. The problem is formulated in a Bayesian framework by modeling the collection of states as a random finite set. First, we propose a multiple sensor Multi-Bernoulli (MS-MeMber) filter based track-before-detect (TBD) algorithm suitable for both MIMO radars and polarimetric MIMO radars. Then the sequential Monte Carlo (SMC) implementations are performed to prove the effectiveness of the proposed algorithm. Simulation results show that the polarization diversity can be exploited to enhance the detecting and tracking performance of MIMO radars.

The moving target detection based on compressed sending (CS) multiple-input multiple output (MIMO) radar with widely separated antennas is studied. The centralized processing is considered. The traditional centralized MIMO radar requires an exhaustive search on every possible two dimensional target velocity, resulting in huge computational complexity. In this paper, we adopt the CS technique to drastically reduce the computational burden and evaluate the reduction of the required complexity. We derive the moving target detector for the CS MIMO radar. The performance of the proposed detector is analyzed, and the receiver operating characteristic (ROC) curves are provided.

Multiple Input-Multiple Output (MIMO) radar is the recent emergence of a new radar technology. This radar system is a new concept advanced for the inadequacy of the traditional system, it use spatial diversity and angel diversity to avoid multipath fading. In this paper, we mainly analyze the detection performance of MIMO radar, and at the same time compared its detection performance with phased array radar under the same conditions. This paper first established MIMO radar’s signal model based on the distribution of sources, and then analyzes the affect of radar’s detection performance through changing the number of antenna. The simulation result showed that the MIMO radar has a good detection performance compared with phased array radar in low signal-noise-ratio(SNR).

A reduced-dimension RML method for transmit beamspace-based MIMO radar

MIMO radar can provide enhanced spatial resolution and achieve better target detection performance. Hence the parameter identifiability of the system can be significantly improved. In this paper, the issue of direction of arrival (DOA) estimation in multiple-input multiple-output (MIMO) radar is discussed, and several algorithms for DOA tracking based on monostatic MIMO radar in impulsive noise background are proposed and studied. These tracking algorithms are based on the concept of robust covariation (ROC), fractional lower order moment (FLOM), fractional lower order cyclic (FLOC) and similar normalization. The transmit and receive arrays of a monostatic narrowband MIMO radar are assumed to be close to each other and eigenstructure based MUSIC algorithm at the receiver is used to identify the DOA's of the targets of interest. Simulation results verify the usefulness of the proposed algorithms. The comparison of these four tracking algorithms show that the algorithm based on the concept of similar normalization has better performance than the others.

For airborne radar systems, space-time adaptive processing (STAP) is the approach that is normally adopted to detect ground moving targets in ground clutter, but the technique becomes less effective if the speed of ground moving targets is very low because of the proximity of the Doppler frequency of targets to that of ground clutter. Typical low-speed ground target detection scenarios include detecting ground troops and slowly-moving ground vehicles. Multiple-Input Multiple-Output (MIMO) radar possesses multiple transmitting and receiving channels providing additional diversified reflections for fusion processing in target detection and measurements. MIMO radar has been extensively investigated [1, 2]; but the exact structure of MIMO radar is defined widely different. In this work, an innovative multi-level MIMO radar signal processing architecture is proposed for airborne systems to effectively detect low-speed ground moving targets. The new MIMO-based approach is demonstrated to be effective for detecting such low-speed ground targets in this work, and some simulation results are presented subsequently.

Multiple-input multiple-output (MIMO) radars have attracted much attention for their superior ability to enhance a system's performance. In this study, the authors' goal was the study of the spatial multiplexing gain of MIMO radars with widely separated antennas (WS-MIMO), which the authors showed that is equal to the number of unambiguously detectable targets. They obtained this number from two different aspects: first, by defining the ambiguity function of a WS-MIMO radar in the case of multiple targets, suitable for such purpose; Second, by modelling the MIMO radar system with a MIMO wireless channel. They showed that a MIMO radar is indeed a MIMO wireless system communicating the information about the existence of the targets. By such modelling, they could easily make the relation between dual concepts of MIMO radar and MIMO communication, one of which is the multiplexing gain.

기존의 위상 배열 레이더는 인접한 안테나 단일 파형의 위상차를 이용하여 특정 방향으로 신호를 집중시키는 방식이다. 반면, MIMO(Multiple-Input Multiple-Output) 레이더는 다수 파형을 넓게 분포한 안테나를 통해 전송하는 방식이다. 이때, 다수의 파형과 넓게 분포한 안테나로부터 발생한 공간 다이버시티를 높은 SNR (Signal-Noise Ratio) 영역에서는 위상 배열 레이더보다 우수한 타켓 검출 성능을 보인다. 하지만, 낮은 SNR 영역에서는 신호를 집중시켜 어레이 이득을 얻는 위상 배열 레이더가 더 좋은 성능을 나타낸다. 본 논문에서는 MIMO 레이더에 위상 배열 안테나를 결합하여 다이버시티 이득과 어레이 이득을 모두 얻을 수 있는 지향성 MIMO 레이더의 검출 성능을 비교해 본다. 이와 더불어 실제 시스템에서 발생할 수 있는 지향각 오차에 대한 민감도가 성능에 미치는 영향을 분석허여, RF 장비의 수가 제한적이고 타켓 검출 성능이 제한적인 상황에서 각 레이더 성능을 비교하였다. In a traditional phased array radar, closely spaced antenna elements transmit a scaled version of single waveform to maximize the signal energy. On the contrary, a multiple-input multiple-output (MIMO) radar consists of widely separated antennas and transmits an arbitrary waveform from each antenna element. These waveforms and spatial diversity enable superior capabilities compared with phased array radar. At high signal-to-noise ratio (SNR), the detection performance of the MIMO radar is better than the phased array radar due to the diversity gains. However, the phased array radar outperforms the MIMO radar at low SNR, due to the energy maximization. In this paper, we investigate the compromised scheme between the MIMO radar and the phased array radar. Employing the MIMO radar equipped with phased array elements, the compromised scheme achieves both array gain and diversity gain. Also, we compare the performance degradation when the steering direction is incorrect.

The problem of tracking the direction of arrivals (DOA) and the direction of departure (DOD) of multiple moving targets is considered in bistatic multiple input multiple output (MIMO) radar. A low complexity angle tracking algorithm in bistatic MIMO radar is proposed. The proposed algorithm derived the difference between previous and current covariance matrices. And the relationship between the different of covariance matrices and the different of target angle is derived. The performance of the proposed algorithm is improved by averaging the covariance matrix elements. Last DOD and DOA are got by least square method. This algorithm avoids the decomposition of the covariance matrix and reduces the computational complexity, which realizes DOA and DOD of the adjacent moment automatic association. The simulation results demonstrate effectiveness of the proposed algorithm. The algorithm provides the technical support for the practical application of MIMO radar.

Multiple Input Multiple Output (MIMO) radar is a new radar technique. It has many advantages over conventional phased radar in many ways such as anti-intercept of radar signal, low velocity target detection, and resolution. The signal transmit matrix of MIMO radar has much information such as target angle. In this paper, we analyze the signal model for MIMO radar. And then we present a new method to estimate the target angle from the matrix. At last, we show the efficiency by some simulations.

Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods.

The colocated multiple input multiple output (MIMO) radar can simultaneously obtain the multi-dimensional information of target for its diversity advantage. Combining the idea of MIMO radar and multiple pulse repetition frequency (PRF), this paper uses multiple orthogonal signals with different medium PRF simultaneously to obtain the target's range and velocity information of different PRF through matched filtering at the receiver. In the simulations, four orthogonal signals at four different PRF are transmitted to obtain the range and velocity information from the total echo signal. All of the results show the feasibility of this model.