Dongjin Wang

Microwave Staring Correlated Imaging (MSCI) is a newly proposed computational high-resolution imaging technique. The imaging performance of MSCI with the existence of modeling errors depends on the properties of the imaging matrix and the relative perturbation error resulted from existing errors. In conventional transient-radiation-fields-based MSCI, which is commonly accomplished by utilizing random frequency-hopping (FH) waveforms, the multiple transmitters should be controlled individually and simultaneously. System complexity and control difficulty are hence increased, and various types of modeling errors are introduced as well. The computation accuracy of radiation fields is heavily worsened by the modeling errors, and the transient effect makes it hard to take direct and high-precision measurements of the radiation fields and calibrate the modeling errors with the measuring result. To simplify the system complexity and reduce error sources, in this paper, steady-radiation-fields-sequence-based MSCI (SRFS-MSCI) method is proposed. The multiple transmitters are excited with coherent signals at the same observation moment, with the signal frequency varying in the whole frequency band during the imaging process. By elaborately designing the array configuration and the amplitude and phase sequences of the coherent transmitters, the SRFS-MSCI is thus implemented. Comparing the system architecture of the proposed SRFS-MSCI with the conventional random FH-based MSCI, it can be found that the proposed method significantly reduces the number of baseband modules and simplifies the system architecture and control logic, which contributes to reducing error sources such as baseband synchronization errors and decreasing deterioration caused by error cascade. To further optimize the design parameters in the proposed SRFS-MSCI system, the Simulated Annealing (SA) algorithm is utilized to optimize the amplitude sequences, the phase sequences, and the antenna positions individually and jointly. Numerical imaging experiments and real-world imaging experiment demonstrate the effectiveness of the proposed SRFS-MSCI method that recognizable high-resolution recovery results are obtained with simplified system structure and optimized system parameters.

As a newly proposed high resolution computational imaging technique, Microwave Staring Correlated Imaging (MSCI) requires elaborate design of the temporal-spatial stochastic radiation fields (TSSRF). Conventional implementation of MSCI radiation source consists of multiple transmitters configuration and simultaneous emitting scheme, which remains underutilization of the available design resources. In this paper, a time-division MSCI (TD-MSCI) method is proposed to virtually generate the TSSRF and make further use of the available design resources. Different from convention simultaneously transmitting based MSCI, the detection scheme in TD-MSCI is achieved by alternately transmitting of each transmitter, rather than simultaneous transmission of multiple transmitters. Echo enhancement is conducted to improve the SNR level of the echo waveform and to resolve the power coastdown caused by single transmitting scheme. Unused combinations of existing resources are digitally synthesized in computer rather than taking extra observations in actual physical environment. After the processing of time division observation, echo enhancement and digital waveform synthesis, extending TSSRF with better stochastic characteristics are generated, and improved imaging performance is obtained. Numerical imaging experiments and preliminary real-world imaging experiment verify the effectiveness of the proposed TD-MSCI, demonstrating that the proposed method increases the flexibility in radiation source design and decreases the detection costs.

Microwave Staring Correlated Imaging (MSCI) technology has attracted increasing attentions for its ability to acquire high-resolution images in staring imaging geometry. The key point of MSCI is to construct the temporal-spatial stochastic radiation field which can be realized by multi-transmitter systems emitting frequency-hopping (FH) waveforms. However, time synchronization errors always exist in practice, which heavily deteriorates the imaging results. Therefore in this paper, a two–step self–calibration imaging method for MSCI transmitting frequency-hopping waveforms is proposed to directly estimate the time synchronization errors. At the first step, a sparsity driven algorithm is proposed which utilizes a novel cluster sparse prior to improve the imaging performance. At the second step, a self–calibration operation is proposed the first time to directly compensate the time synchronization errors in MSCI. By alternately reconstructing the targets and estimating the time synchronization errors, the proposed method can reconstruct the target with high imaging quality and effectively compensate the time synchronization errors. Numerical simulations demonstrate the potential advantages of the proposed method to enhance the imaging quality and improve the time synchronization error estimation performance.

Microwave staring correlated imaging (MSCI), with the technical capability of high-resolution imaging on relatively stationary targets, is a promising approach for remote sensing. For the purpose of continuous observation of a fixed key area, a tethered floating aerostat is often used as the carrying platform for MSCI radar system; however, its non-cooperative random motion of the platform caused by winds and its unbalance will result in blurred imaging, and even in imaging failure. This paper presents a method that takes into account the instabilities of the platform, combined with an adaptive variable suspension (AVS) and a position and orientation system (POS), which can automatically control the antenna beam orientation to the target area and measure dynamically the position and attitude of the stochastic radiation radar array, respectively. By analyzing the motion feature of aerostat platform, the motion model of the radar array is established, then its real-time position vector and attitude angles of each antenna can be represented; meanwhile the selection matrix of beam coverage is introduced to indicate the dynamic illumination of the radar antenna beam in the overall imaging area. Due to the low-speed discrete POS data, a curve-fitting algorithm can be used to estimate its accurate position vector and attitude of each antenna at each high-speed sampling time during the imaging period. Finally, the MSCI model based on the unsteady aerostat platform is set up. In the simulations, the proposed scheme is validated such that under the influence of different unstable platform movements, a better imaging performance can be achieved compared with the conventional MSCI method.

Microwave staring correlated imaging (MSCI) can realize super resolution imaging without the limit of relative motion with the target. However, gain–phase errors generally exist in the multi-transmitter array, which results in imaging model mismatch and degrades the imaging performance considerably. In order to solve the problem of MSCI with gain–phase error in a large scene, a method of MSCI with strip-mode self-calibration of gain–phase errors is proposed. The method divides the whole imaging scene into multiple imaging strips, then the strip target scattering coefficient and the gain–phase errors are combined into a multi-parameter optimization problem that can be solved by alternate iteration, and the error estimation results of the previous strip can be carried into the next strip as the initial value. All strips are processed in multiple rounds, and the gain–phase error estimation results of the last strip can be taken as the initial value and substituted into the first strip for the correlated processing of the next round. Finally, the whole imaging in a large scene can be achieved by multi-strip image splicing. Numerical simulations validate its potential advantages to shorten the imaging time dramatically and improve the imaging and gain–phase error estimation performance.

The stochastic characteristic of the radiation field of a mono-static microwave staring correlated imaging (MSCI) radar degenerates with the increase of the imaging distance, which results in degradation of the image quality. To address this issue, a novel MSCI method based on bi-static radar is proposed from two perspectives: site-deploying and waveform design. On the one hand, a new bi-static MSCI site-deploying scheme is proposed which adopts two transmitting stations with their azimuth angles relative to the center of the imaging region differing by 90 degrees. On the other hand, by using two transmitting arrays synchronously transmitting inner-and-inter pulse frequency hopping (IAIP-FH) signals, the radiation field of each station includes a few “frequency stripes” perpendicular to the radiation direction, and as a consequence, the “frequency stripes” of each radiation field are perpendicular to each other. As a result, the radiation field of the bi-static MSCI is the superposition of the two striped radiation fields, thus a latticed radiation field is constructed. Therefore, the targets in different latticed grids scatter independent fields, then, the images can be reconstructed using correlation process (CP) algorithms. The grid size of the latticed radiation field is determined by the inner-pulse frequency hopping (FH) interval of the IAIP-FH signals and the imaging geometry. Moreover, it is shown that the 3 dB beam width of the space correlation function of the radiation field does not change with the imaging distance, thus the stochastic characteristic of the radiation field is partly preserved when the imaging distance increases. Simulation results validate the analysis and show that the proposed method can obtain higher resolution images than the common mono-static MSCI method.

Microwave staring correlated imaging (MSCI) can achieve ultra-high resolution in real aperture staring radar imaging using the correlated imaging process (CIP) under all-weather and all-day circumstances. The CIP must combine the received echo signal with the temporal-spatial stochastic radiation field. However, a precondition of the CIP is that the continuous imaging region must be discretized to a fine grid, and the measurement matrix should be accurately computed, which makes the imaging process highly complex when the MSCI system observes a wide area. This paper proposes an adaptive imaging approach for the targets in discrete clusters to reduce the complexity of the CIP. The approach is divided into two main stages. First, as discrete clustered targets are distributed in different range strips in the imaging region, the transmitters of the MSCI emit narrow-pulse waveforms to separate the echoes of the targets in different strips in the time domain; using spectral entropy, a modified method robust against noise is put forward to detect the echoes of the discrete clustered targets, based on which the strips with targets can be adaptively located. Second, in a strip with targets, the matched filter reconstruction algorithm is used to locate the regions with targets, and only the regions of interest are discretized to a fine grid; sparse recovery is used, and the band exclusion is used to maintain the non-correlation of the dictionary. Simulation results are presented to demonstrate that the proposed approach can accurately and adaptively locate the regions with targets and obtain high-quality reconstructed images.

The frequency diverse multiple-input-multiple-output (FD-MIMO) radar synthesizes a wideband waveform by transmitting and receiving multiple frequency signals simultaneously. For FD-MIMO radar imaging, conventional imaging methods based on Matched Filter (MF) cannot enjoy good imaging performance owing to the few and incomplete wavenumber-domain coverage. Higher resolution and better imaging performance can be obtained by exploiting the sparsity of the target. However, good sparse recovery performance is based on the assumption that the scatterers of the target are positioned at the pre-discretized grid locations; otherwise, the performance would significantly degrade. Here, we propose a novel approach of sparse adaptive calibration recovery via iterative maximum a posteriori (SACR-iMAP) for the general off-grid FD-MIMO radar imaging. SACR-iMAP contains three loop stages: sparse recovery, off-grid errors calibration and parameter update. The convergence and the initialization of the method are also discussed. Numerical simulations are carried out to verify the effectiveness of the proposed method.

Due to some adverse factors, tracking multitarget in dense clutter becomes a difficult task. Therefore, a new algorithm is presented in this paper which consists of m-best 2-D assignment algorithm and generalized likelihood ratio test (GLRT). Simulated numerical examples are given, which demonstrate that this algorithm can solve the problem of "measurement-to-track" association efficiently, and discriminate the true or false track in short time which can reduce the need for storage and calculation enormously

In this paper we present a frequency-domain high resolution TOA estimation approach using TLS-ESPRIT based on FBCM matrix. TLS-ESPRIT is applied to perform the unknown frequencies estimation, i.e., directions of arrival, the time delays, hence to estimate TOA. Since within the situation of small sample, the FBCM has good decorrelation effects, it will be selected as correlation method to improve the performance of estimation, meanwhile the slide window also be considered. The performance of super-resolution techniques is evaluated with the conventional TOA estimation techniques. The simulations show that the proposed approach provides high resolution time of arrival estimations