Multistatic Radar Imaging Research Articles

In Situ Monitoring of Channel Imbalances of Air/Space-Borne Antenna Arrays Through Multistatic Radar Imaging

In Situ Monitoring of Channel Imbalances of Air/Space-Borne Antenna Arrays Through Multistatic Radar Imaging

A New Device for Two-Way Time-Frequency Real-Time Synchronization

<p>The netted wireless sensor nodes or coherent accumulation processing in multistatic radar imaging requires high accuracy time synchronization. Although GNSS timing can also be used as a time synchronization method to serve the applications above, its timing accuracy will be limited. In this context, we present the hardware implementation for Two-Way Time-Frequency Real-Time Synchronization (TWTFRTS) with an automatic adaptive jitter elimination algorithm based on Kalman and PID, which is implemented in a real-time, low-cost, portable Xilinx ZYNQ device. A short (2 km) baseline TWTFRTS experiment was done with a pair of devices composed of a master device and a slave device. The result shows a high precision of time synchronization performance with the standard deviation (1 σ) better than 1 ns.</p> <p> </p>

Passive Multistatic Radar Imaging of Vessel Target Using GNSS Satellites of Opportunity

The global navigation satellite system (GNSS)-based passive radar shows potential in permanent maritime surveillance. In this paper, the GNSS signals are exploited for vessel target imaging. From the obtained radar image, meaningful information about the vessel, such as its shape, position, length, and orientation can be extracted. In addition, the vessel is observed from different angles by spatially diverse GNSS satellites, and the multistatic geometry enables to enhance the imagery quality. The main drawback of GNSS-based passive radar stays in its limited power budget. And the inaccessible motion makes the noncooperative vessel smeared using conventional radar imaging methods. To address the problems, at first, each bistatic echo over a long observation time is integrated in range and Doppler (RD) domain after removing the two-dimensional migrations. The signal-to-noise ratio can be increased after the step. Then, with respect to a particular target velocity, the local Cartesian plane is constructed, and the multiple RD maps are projected and combined in the plane to obtain the multistatic image. In view of the inaccessibility of target kinematic parameters, such imaging processing is modeled as an optimization problem, where vessel’s velocity is set as decision variable and the aim is to minimize the image entropy. Finally, particle swarm optimization (PSO) algorithm is applied to solve the optimization problem, after which a well-focused vessel image can be obtained. In May 2021, we have successfully carried out the world’s first BeiDou-based passive radar maritime experiment, and effectiveness of the proposed method is verified against the experimental data.

Waveform Optimization for Multistatic Radar Imaging Using Mutual Information

This article addresses the problem of radar waveform design for imaging targets in a cluttered environment. A multistatic radar scenario is considered for sparse and random sensor positions. The target impulse response is modeled using the simulated frequency response of a buried metallic mine, and the clutter is modeled using the compound Gaussian (CG) distribution. We explore novel waveform design techniques with respect to the mutual information (MI) criterion based on the CG clutter. The first method presents a waveform that exploits the CG distribution of the scene reflectivity function when projected onto a sparse basis. This is compared to the second method, called the target-specific approach that uses knowledge of the target and clutter frequency responses to optimize a matched illumination waveform. In both cases, the Taguchi and particle swarm optimization solvers are employed for MI-based waveform design optimization. To validate and compare the effectiveness of the optimized waveforms, the resulting scene reflectivity function is estimated using the sparsity-driven regularization radar imaging method. Our experimental results demonstrate that both waveform optimization techniques result in significantly better image reconstruction performance than the traditional linear-frequency-modulated waveform, and that the target-specific approach additionally suppresses clutter information in the scene.

Group adaptive matching pursuit with intra-group correlation learning for sparse signal recovery

In this paper, a Bayesian model is adopted for sparse signal recovery where sparsity is enforced on the reconstructed coefficients via probabilistic priors. In particular, we focus on a group spike-and-slab prior and a kernel matrix which capture both the underlying group structure and the element correlation within groups. A novel greedy based group adaptive matching pursuit (GAMP) algorithm is introduced, which integrates both prior parameter learning and intra-group correlation parameter learning into one single problem. The proposed approach improves the reconstruction accuracy and offers strong robustness to signal-to-noise ratio. We consider a fast implementation method of GAMP which applies the preconditioned conjugate gradient method. Simulations, MNIST dataset based experiments and multi-static radar imaging application are used to verify the superior performance of the proposed method over existing techniques.

Exact Multistatic Interferometric Imaging via Generalized Wirtinger Flow

In this paper, we present a novel approach that can exactly recover extended targets in wave-based multistatic interferometric imaging, based on Generalized Wirtinger Flow (GWF) theory [1]. Interferometric imaging is a generalization of phase retrieval, which arises from cross-correlation of measurements from pairs of receivers in multistatic configuration. Unlike standard Wirtinger Flow, GWF theory guarantees exact recovery for arbitrary lifted forward models that satisfy the restricted isometry property over rank-1, positive semi-definite (PSD) matrices with a sufficiently small restricted isometry constant (RIC). To this end, we design a deterministic, lifted forward model for interferometric multistatic radar satisfying the exact recovery conditions of the GWF theory. Our results quantify a lower limit on the pixel spacing and the minimal sample complexity for exact multistatic radar imaging via GWF. We provide a numerical study of our RIC and pixel spacing bounds, which shows that GWF can achieve exact recovery with super-resolution. While our primary interest lies in radar imaging, our method is also applicable to other multistatic wave-based imaging problems such as those arising in acoustics and geophysics.

A Generalization of Wirtinger Flow for Exact Interferometric Inversion

Interferometric inversion involves recovery of a signal from cross-correlations of its linear transformations. A close relative of interferometric inversion is the generalized phase retrieval problem, which consists of recovering a signal from the auto-correlations of its linear transformations. Recently, significant advancements have been made in phase retrieval methods despite the ill-posed, and non-convex nature of the problem. One such method is Wirtinger Flow (WF), a non-convex optimization framework that provides high probability guarantees of exact recovery under certain measurement models, such as coded diffraction patterns, and Gaussian sampling vectors. In this paper, we develop a generalization of WF for interferometric inversion, which we refer to as Generalized Wirtinger Flow (GWF). GWF theory extends the probabilistic exact recovery results of WF to arbitrary measurement models characterized in the equivalent lifted problem, hence covers a larger class of measurement models. Our framework unifies the theory of low rank matrix recovery (LRMR) and the non-convex optimization approach of WF, thereby establishes theoretical advantages of the non-convex approach over LRMR. We identify a new sufficient condition on the lifted forward model that directly implies exact recovery conditions of standard WF. This condition is less stringent than those of LRMR, which is the state of the art approach for exact interferometric inversion. We establish our sufficient condition for the cross-correlations of linear measurements collected by complex Gaussian sampling vectors, and show that the exact recovery conditions of standard WF imply our sufficient condition. As a result, the regularity condition of WF becomes redundant in solving the interferometric inversion problem. Finally, we demonstrate the effectiveness of GWF numerically in a deterministic multi-static radar imaging scenario.

Joint compressed sensing imaging and phase adjustment via an iterative method for multistatic passive radar

The resolution of the multistatic passive radar imaging system (MPRIS) is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity. Moreover, the inaccuracies caused by the inaccurate tracking system or the error position measurement of illuminators or receivers can deteriorate the quality of an image. To improve the performance of an MPRIS, an imaging method based on the tomographic imaging principle is presented. Then the compressed sensing technique is extended to the MPRIS to realize high-resolution imaging. Furthermore, a phase correction technique is developed for compensating for phase errors in an MPRIS. Phase errors can be estimated by iteratively solving an equation that is derived by minimizing the mean recovery error of the reconstructed image based on the principle of fixed-point iteration technique. The technique is nonparametric and can be used to estimate phase errors of any form. The effectiveness and convergence of the technique are confirmed by numerical simulations.

Phase adjustment for multistatic passive radar imaging based on image entropy and image contrast

ABSTRACTMultistatic passive radar imaging system (MPRIS) does not behave as well as traditional inverse synthetic aperture radar (ISAR) imaging system does on account of the limited illuminators of opportunity and relatively narrow bandwidth of the signals. Furthermore, in the MPRIS, limited by the unavoidable measurement errors of the positions of the illuminators or receivers, or the manoeuvrability of the moving target, the translation motion of the target cannot be compensated accurately, in which the echo will be contaminated by an error phase, resulting in the occurrence of serious image defocus and the degeneracy of the image quality. In this article, two nonparametric autofocusing techniques based on the principles of minimum entropy and maximum contrast, respectively, are proposed to deal with the undesired problems under the multistate geographical configuration consisting of one illuminator and several receivers distributed around a target. The error phase is corrected by introducing an adjustment phase in the frequency domain and the adjustment phase can be searched by iteratively solving an equation that is derived by optimizing the quality measure of the image. Since no assumption is made during the derivation, the proposed techniques could be used to compensate any form of the phase error. In addition, the influence of the multistatic configuration on the image resolution is analysed and an optimized configuration is obtained. The effectiveness of the proposed algorithms is verified via the theoretical derivations and simulations.

Compound Radar Approach for Breast Imaging

Multistatic radar apertures record scattering at a number of receivers when the target is illuminated by a single transmitter, providing more scattering information than its monostatic counterpart per transmission angle. This paper considers the well-known problem of detecting tumor targets within breast phantoms using multistatic radar. To accurately image potentially cancerous targets size within the breast, a significant number of multistatic channels are required in order to adequately calibrate-out unwanted skin reflections, increase the immunity to clutter, and increase the dynamic range of a breast radar imaging system. However, increasing the density of antennas within a physical array is inevitably limited by the geometry of the antenna elements designed to operate with biological tissues at microwave frequencies. A novel compound imaging approach is presented to overcome these physical constraints and improve the imaging capabilities of a multistatic radar imaging modality for breast scanning applications. The number of transmit-receive (TX-RX) paths available for imaging are increased by performing a number of breast scans with varying array positions. A skin calibration method is presented to reduce the influence of skin reflections from each channel. Calibrated signals are applied to receive a beamforming method, compounding the data from each scan to produce a microwave radar breast profile. The proposed imaging method is evaluated with experimental data obtained from constructed phantoms of varying complexity, skin contour asymmetries, and challenging tumor positions and sizes. For each imaging scenario outlined in this study, the proposed compound imaging technique improves skin calibration, clearly detects small targets, and substantially reduces the level of undesirable clutter within the profile.

GROUND MOVING TRAIN IMAGING BY KU-BAND RADAR WITH TWO RECEIVING CHANNELS

Radar imaging experiment of ground moving target a light rail-way train by Ku-band radar with two receiving channels is introduced with imaging results presented. Both coherent and incoherent imaging as well as co-pol and cross-pol interferometric imaging were conducted with SAR amplitude images as well as interferometric-phase images obtained and compared. The along-track interferometric-phase images clearly show the speed variation of the train, i.e., the train traveled at an increasing speed in one direction and at a decreasing speed in the opposite direction. The incoherent imaging results indicate that for bistatic or multi-static radar imaging, the ultra-stable crystal oscillator (USCO) used by transmitter need not to be synchronized to the USCO used by receiver provided that the two basic crystal oscillators have small frequency difference and high stability. For better knowing the speed variation, Short Time Fourier Transform (STFT) is applied to the azimuthal signals to get the instant Doppler frequencies (IDFs), from which one can judge acceleration or deceleration status of the moving train. Electromagnetic scattering characteristics of the train are analyzed according to the SAR images. The estimated speed and length of the train are very well agreed with real situation.

Multistatic Radar Imaging of Moving Targets

We develop a linearized imaging theory that combines the spatial, temporal, and spectral aspects of scattered waves. We consider the case of fixed sensors and a general distribution of objects, each undergoing linear motion; thus the theory deals with imaging distributions in phase space. We derive a model for the data that is appropriate for narrowband waveforms in the case when the targets are moving slowly relative to the speed of light. From this model, we develop a phase-space imaging formula that can be interpreted in terms of filtered backprojection or matched filtering. For this imaging approach, we derive the corresponding phase-space point-spread function (PSF). We show plots of the phase-space point-spread function for various geometries. We also show that in special cases, the theory reduces to: 1) range-Doppler imaging, 2) inverse synthetic aperture radar (ISAR), 3) synthetic aperture radar (SAR), 4) Doppler SAR, and 5) tomography of moving targets.

Ultrawideband Multi-Static Scattering Analysis of Human Movement Within Buildings for the Purpose of Stand-Off Detection and Localization

An efficient and accurate method for the analysis of human backscatter response to ultrawideband near-field UHF radar systems is presented. The method combines a full-wave analysis of a human body model with a PO-like indoor propagation code known as “brick-tracing.” This method allows for the analysis of radar detection and tracking of humans within a realistic building environment. Using this method multi-static radar imaging of a human in buildings is evaluated and certain special features are pointed out. The simulation results are validated with actual radar measurements of a mannequin in a lab environment. A viable method for real time detection and localization of moving human inside buildings is then applied to the simulated and measured data and the building effects on the detection and localization are discussed.