The design and optimization of sensor array configurations is a significant challenge for distributed SAR-GMTI radar systems because the system performance of distributed array radar is a comprehensive result of several conflicting evaluation indicators. This paper developed a multi-objective intelligent optimization method to solve the global optimal problem of array configurations in terms of achieving optimal GMTI performance. Firstly, to formulate the relationship between array configuration and GMTI performance, we established three objective functions derived from evaluating indicators of SAR-GMTI performance. Specifically, in the objective functions, we proposed a novel clutter covariance matrix model that added several typical non-ideal factors of the real-world detection environment. This provides a way to build a bridge between the array configuration, environment clutter, and GMTI performance. Then, we proposed an improved multi-objective snake optimization algorithm (IMOSOA) that combined the Pareto optimization mechanism with snake optimization to solve the multi-objective optimization problem while reconciling the conflicts between different objective functions. Meanwhile, some significant improvements were made to speed up convergence. That is, tent chaotic mapping-based initialization, multi-group coevolution, and individual mutation strategies were applied to solve the non-convergence problem of global searching. Finally, in the case of an airborne SAR-GMTI system, numerical experiments demonstrated that the proposed IMOSOA has superior performance than other contrast methods, especially in terms of GMTI applications.
Enzyme therapy has important implications for the treatment of metabolic disorders and biological detoxification. It remains challenging to prepare enzymatic nanoreactors with high therapeutic efficiency and low emission of cytotoxic reaction intermediates. Here, we propose a novel strategy for the preparation of enzymes-loaded polypeptide microcapsules (EPM) with concentrically encapsulated enzymes to achieve higher cascade reaction rates and minimal emission of cytotoxic intermediates. Mesoporous silica spheres (MSS) are used as a highly porous matrix to efficiently load a therapeutic enzyme (glucose oxidase, GOx), and a layer-by-layer (LbL) assembly strategy is employed to assemble the scavenging enzyme (catalase) and polyelectrolyte multilayers on the MSS surface. After removal of the MSS, a concentrically encapsulated EPM is obtained with the therapeutic enzyme encapsulated inside the capsule, and the scavenging enzyme immobilized in the polypeptide multilayer shell. Performance of the concentrically encapsulated GOx-catalase capsules is investigated for synergistic glucose metabolism disturbance correction and cytotoxic intermediate H2O2 clearance. The results show that the EPM can simultaneously achieve 99% H2O2 clearance and doubled glucose consumption rate. This strategy can be extended to the preparation of other dual- or multi-enzyme therapeutic nanoreactors, showing great promise in the treatment of metabolic disorders.
The fluent ship targets with micro-motion which is caused by oceanic waves leading to defocused images. Due to the large size ship, there is a multi-component echo signal in one range bin, thus it is crucial to extract the micro-Doppler (m-D) features quickly and precisely to refocus the images. This paper puts forward a novel micro-motion feature extraction and estimation method. The method is composed of two steps, and the first step is preprocessing to do the Short-Time Fourier Transform (STFT). After that, we propose a new form of synchrosqueezing transform to concentrate the energy spread curves which can be established as a state translation model. Then in the second step, we use the RFS-based Bernoulli filter to estimate the parameters of the multi-component signal. In this step, the method avoids the disturbance of stray points and empty areas so that the m-D parameters can be estimated accurately. The experimental results prove the availability of the proposed method and the accuracy of the estimation of m-D parameters.
For the long-range and high-speed maneuvering space target detection, its performance is constrained by two challenges, the coherent integration loss due to Range migration and Doppler frequency spread, the contradiction between Range Ambiguity, and Doppler bandwidth ambiguity. This paper proposed a novel parameter estimation and integration method to solve the above two issues. Firstly, the waveform diversity technique is used to solve the range ambiguity problem. Then a matched filter bank for high-speed targets that can calculate the range ambiguity number is designed. Next, coherent integration is successfully achieved by estimating the target motion parameters and compensating for the echo signal. Finally, a two-step search strategy is proposed to improve the parameter estimation process, numerical experiments have validated that the complexity of the algorithm is reduced while ensuring the accuracy of the parameter estimation.
This study focuses on the coherent integration (CI) of space-based distributed radar for space high-speed maneuvering targets. The platform space difference and high-speed motion result in the envelope position offset and phase difference of the inter-channel signals. Meanwhile, the high-speed and maneuvering characteristics of the target will lead to the range walk (RW) and doppler walk (DW) in the pulse accumulation in single node, which brings challenges to the detection of space targets. This paper presents a method for spatial-temporal joint coherent integration and parameter estimation based on the time-space range history. Firstly, based on the coupling relationship of spatial and temporal in the range history, we established inter-channel envelope correction and phase compensation function to address the inter-channel echo range and phase difference caused by spatial location differences between nodes. Secondly, we combined inter-channel synthetic compensation with generalized Radon-Fourier transform (GRFT). Thus, target range, angle, velocity, and acceleration parameter estimation are integrated into a unified framework. Finally, joint Spatial-temporal joint CI and parameter estimation for space-borne distributed radar system can be implemented. Simulation results demonstrate the effectiveness of the proposed method.
In multi-satellite formation synthetic aperture radar (SAR) systems, terrain interferometric phase is a crucial factor that degrades the performance of multi-channels coherence processing. To address this issue, we developed a high-precision self-adaptive topographic phase compensation (TPC) method. It emphasizes minimizing the topographic phase estimation error based on an error feedback weighting correction structure. The self-adaptive weighting coefficient is constructed using image coherence, phase residual distribution, and fringe structural features. This ensures that the phase estimation error component will be corrected in an extremely heterogeneous observation scene. The experimental results of multi-satellite SAR image simulated data demonstrate that the proposed method can significantly improve the accuracy of the topographic phase compensation.
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