A Novel Transmitter-Interpulse Phase Coding MIMO-Radar for Range Ambiguity Separation

A New Algorithm Based on Cyclic Statistics for Two-Dimensional MIMO Radar

Multiple-input and multiple-output (MIMO) radar is a new radar system which is proposed in recent years. Taking advantages of waveform diversity, MIMO radar can improve the identifiability of the parameter and the adaptive technology for receiving data on radar, and enhance the parameter estimation and target detection performance. In this paper, several traditional nonparametric adaptive technologies are introduced in parameter estimation method. In the case of a known number of targets, a multi-target parameter estimation method that combines Capon, Generalized-Likelihood Ratio (GLR) and approximate maximum likelihood (AML) estimation is proposed, which is called CGAML. It can estimate the target position and the corresponding complex amplitude accurately. And then a target detection model is established under multi-pulse accumulation conditions of MIMO radar, which analyses the performance of MIMO radar on target detection. Compared with the conventional phased array radar, MIMO radar system shows better advantages.

A microwave photonic multiple-input and multiple-output (MIMO) radar is proposed and demonstrated to implement high-resolution imaging. In the proposed system, multiple orthogonal linearly frequency modulated (LFM) signals are generated by heterodyning between two optical frequency combs, which enables a MIMO transmitting array with a simple and reconfigurable structure. The receiving array uses photonic frequency mixing to implement multiple channel separation and de-chirp processing simultaneously. This microwave photonic MIMO radar can have a large operation bandwidth and a large equivalent aperture, which helps to achieve high-resolution imaging in both range and azimuth directions. In the experiment, a microwave photonic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{4}\times \text{8}$</tex-math></inline-formula> MIMO radar is established with a 2-GHz bandwidth in each channel. Based on this MIMO radar, high-resolution back-projection (BP) imaging with a theoretical range resolution of 7.5 cm and azimuth resolution of 1.85° is demonstrated. The experimental results can verify the feasibility of the proposed MIMO radar, which is a good solution to high-resolution radar imaging by combining microwave photonic and MIMO technologies.

In this research, we introduce a signal processing framework for joint GMTI and SAR algorithms that is based on orthogonal (transmit and receive) waveforms. Traditionally, radar systems are configured to operate either in GMTI or SAR processing mode, but not both simultaneously. This is due to the fact that operational parameters for these two modes are quite different. For example, exoclutter GMTI processing requires a high pulse repetition frequency (PRF), but a high PRF results in increased range ambiguity - and an increased processing burden - in SAR imaging. We propose combining diverse, orthogonal waveforms and introducing corresponding processing techniques to reduce the problems and complexities of joint GMTI and SAR exploitation. For the exoclutter GMTI problem, the necessary high-PRF pulse train will be used to achieve finer Doppler resolution for detecting fast moving objects. For the endoclutter GMTI and SAR imaging problem, we will transmit low PRF pulses. The goal for low PRF pulses for endoclutter GMTI and SAR imaging is to ensure that range ambiguity issue has been addressed. These new approaches will achieve following benefits: (1) accomplish GMTI and SAR processing concurrently by eliminating the complexities associated with reconfiguring a radar system, (2) more efficiently use bandwidth by employing appropriate bandwidth for exoclutter GMTI pulses and SAR image formation pulses, and (3) reduce range ambiguity issue associated with high PRF operation.

Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the super-resolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. Then, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot.

Multiple input and multiple output (MIMO) radar can accumulate many more periods than Phased Array Radar. But when the target travels through the beam, the Doppler frequency motion may occur. So Doppler compensation must be done with more accurate acceleration estimation. The fractional Fourier transform (FRFT) methods in the references before are based on the analysis of global spectrum with fixed resolution and do not involve local detail spectrum. This paper puts forward using the united method of radical FRFT, Zoom-FRFT and single point FRFT for acceleration accurate estimation in MIMO radar.

MIMO Radar with Widely Separated Antennas—From Concepts to Designs

Objectives. In recent years, more and more attention has been paid in radar theory and practice to the development of multiple-input and multiple-output (MIMO) radar, which offers a number of advantages over traditional radar based on phased antenna arrays (PAAs). These include the possibility to flexibly view space and adapt to a changing signal-interference environment, etc. MIMO technology used in radar requires the emission of a probe signal in the form of a coherent system of orthogonal signals, each of which triggers its own emitter in the transmitting antenna array (AA). As a result, the specified target search area is simultaneously illuminated. Specific spatiotemporal processing (SSP) is used to collect signals from all directions in the irradiated zone at the receiver output. In this regard, the task of finding an SSP structure in MIMO radar that is optimal compared to the traditional approach becomes urgent. The study set out to synthesize the structure of SSP with single–channel reception in MIMO radar and compare the obtained structure and characteristics with those similar in traditional parallel-view radars based on multipath receiving radar.Methods. The study is based on methods and principles of the theory of multibeam synthesized aperture antennas and methods for the synthesis of optimal Neiman–Pearson detectors based on the likelihood ratio.Results. For a MIMO radar with AA for transmission and reception provided by a single weakly directional antenna, a split SSP was synthesized to form optimal pre-threshold statistics (PTS) of the detector against a background of white Gaussian noise. The obtained PTS is compared with a similar PTS in a traditional parallel space survey radar with a mirror structure.Conclusions. It is shown that the detection quality indicators of the compared radars in the mirror construction are equivalent in the mode of parallel target search in the same spatial sectors.

Different beamforming techniques are looking forward to playing an important role in the next generation Multiple Input and Multiple Output (MIMO) radar based system. For the enhancement in resolution and for identification of more number of targets, MIMO radar is very useful. We get more flexibility in beampattern design using the MIMO concept in the radar system. Beamforming is used in sensor arrays for signal transmission or reception in particular directions. For improving resolution of target and reducing interference of signals from undesirables,a powerful technique is used in signal processing of MIMO radar known as beamforming. Various sorts of calculations are researched utilizing conventional beamforming and Adaptive beamforming to alter the necessary weighting on radio wire components. Effects of various errors are illustrated. Also, Linearly Constrained Minimum Variance (LCMV) and Minimum Variance Distortionless Variance (MVDR) calculations introduced which are the techniques gone under versatile beamforming and their belongings are talked about. The most useful algorithms which are the versatile techniques for beamforming as mentioned above are discussed in this paper.From the comparison of LCMV and MVDR algorithms with different number of array elements concluded which is better in different situations of the MIMO radar system.

In this paper, the clutter estimation problem in the bistatic multiple-input and multiple-output (MIMO) radar system is considered, and a novel compressed sensing (CS)-based model is proposed to describe the clutter by exploiting the clutter sparsity in the angle domain. Then, the CS-based methods are adopted to reconstruct the sparse clutter, and to estimate the clutter scattering coefficients and angles. Additionally, different from the traditionally colocated MIMO radar system with the antenna distance being half of wavelength, we show that the optimal antenna distance in the CS-based radar system can be obtained by minimizing the mutual coherence of the dictionary matrix. Moreover, since the sparse reconstruction performance depends on the geographical positions of the clutter scatterers, an indirect method based on the mutual coherence is proposed to measure the estimation performance, and to optimize the radar parameters. Simulation results show that the CS-based method can estimate the clutter information efficiently, and the better estimation performance is achieved by optimizing the radar parameters.

Conventional multiple-input and multiple-output (MIMO) radar is a flexible technique which enjoys the advantages of phased-array radar without sacrificing its main advantages. However, due to its range-independent directivity, MIMO radar cannot mitigate nondesirable range-dependent interferences. In this paper, we propose a range-dependent interference suppression approach via frequency diverse array (FDA) MIMO radar, which offers a beamforming-based solution to suppress range-dependent interferences and thus yields much better DOA estimation performance than conventional MIMO radar. More importantly, the interferences located at the same angle but different ranges can be effectively suppressed by the range-dependent beamforming, which cannot be achieved by conventional MIMO radar. The beamforming performance as compared to conventional MIMO radar is examined by analyzing the signal-to-interference-plus-noise ratio (SINR). The Cramér-Rao lower bound (CRLB) is also derived. Numerical results show that the proposed method can efficiently suppress range-dependent interferences and identify range-dependent targets. It is particularly useful in suppressing the undesired strong interferences with equal angle of the desired targets.

Multiple-input and multiple-output (MIMO) radar systems have garnered significant interest due to their ability to generate additional target radar returns. These additional returns have the potential to improve tracking performance in terms of target tracking accuracy and resource use. This effort examines radar pulse control for target tracking optimization in a MIMO radar architecture. Specifically, an optimization algorithm based on the Cramer-Rao lower bound on the target state is implemented. The performance of the optimized tracker is compared to a conventional tracking algorithm in the Benchmark radar simulation environment. The difficulties in achieving significant performance improvements through MIMO techniques in a conventional radar system architectures are elucidated.

The problem of phase synchronization mismatch in multiple-input and multiple-output (MIMO) radar sacrifices severely its detection performance. To overcome such problem, we propose a new phase synchronization algorithm in MIMO radar. The novelty of the proposed algorithm is to achieve final synchronization at target by utilizing only two time slots no matter how many of transmitters, whereas the existing algorithms just achieved the synchronization among transmitters and took more time slots as the number of transmitters increasing. Therefore, the proposed algorithm not only has better performance but also reduces the synchronization overhead obviously as compared with the existing algorithms. Through theoretical derivation and simulation results, the superiority of the proposed algorithm is verified.

In array signal processing, the mutual coupling among physical sensors can inevitably affect the estimation of the direction of arrival (DOA). Despite the fact that multiple-input and multiple-output (MIMO) radar can provide greater degrees of freedom (DOFs), the influence of mutual coupling is largely overlooked in many current MIMO radar designs. To tackle this issue, we propose the utilization of a generalized nested array (GNA) in transmitter array and we introduce an expansion factor into the nested array in the receiver array. Thereby, a novel GNA-MIMO radar is put forward. The proposed MIMO radar offers O(N4) consecutive DOFs with N sensors and avoids the adverse effects of high mutual coupling caused by closely located sensors. Furthermore, we derive the closed-form expressions for the position of physical sensors and the attainable consecutive DOFs of the proposed MIMO radar. Through simulation experiments, we demonstrate the superior accuracy of the proposed MIMO configuration in DOA estimation and angle resolution under the condition of mutual coupling effect.

In this study, an estimation problem for target directions in the multiple-input and multiple-output (MIMO) radar system with unknown mutual coupling is considered. To exploit the target sparsity in the spatial domain, a compressed sensing-based method using multiple measure vectors is proposed to reconstruct the sparse targets and estimate the target directions and scattering coefficients. Additionally, we also propose an iterative method to estimate the mutual coupling matrices after obtaining the target information, where the sub-gradients are derived theoretically to update the mutual coupling matrices. Simulation results show that compared with the exiting methods, the better estimation performance for both target directions and mutual coupling matrices can be achieved by the proposed method.