The use of optical sensing for in vivo applications is compelling, since it offers the advantages of non-invasiveness, non-ionizing radiation, and real-time monitoring. However, the signal-to-noise ratio (SNR) of the optical signal deteriorates dramatically as the biological tissue increases. Although increasing laser power can improve the SNR, intense lasers can severely disturb biological processes and viability. Quantum sensing with bright squeezed light can make the measurement sensitivity break through the quantum noise limit under weak laser conditions. A bright squeezed light source is demonstrated to avoid the deterioration of SNR and biological damage, which integrates an external cavity frequency-doubled laser, a semi-monolithic standing cavity with periodically poled titanyl phosphate (PPKTP), and a balanced homodyne detector (BHD) assembled on a dedicated breadboard. With the rational design of the mechanical elements, the optical layout, and the feedback control equipment, a maximum non-classical noise reduction of −10.7 ± 0.2 dB is observed. The average squeeze of −10 ± 0.2 dB in continuous operation for 60 min is demonstrated. Finally, the intracavity loss of degenerate optical parametric amplifier (DOPA) and the initial bright squeezed light can be calculated to be 0.0021 and −15.5 ± 0.2 dB, respectively. Through the above experimental and theoretical analysis, the direction of improving bright squeeze level is pointed out.

Distributed radar systems promise to significantly enhance target localization by virtue of the superiority of multi-view observations from widely separated radars, compared to their monostatic counterparts. Nevertheless, when the radar number is limited, performing target localization bears the brunt of the parameter identifiability requirement that the parameter number must be no less than the number of independent measurements. In this way, the canonical two-stage target localization method, as well as its developments, is no longer appropriate for direct application. Hence, in this paper, we propose a novel target localization method using time-difference-of-arrival (TDOA) measurements with the minimum number of radars under platform position uncertainties. The referred distributed system is a bistatic multi-receiver system, where the primary signal is transmitted by a geostationary Earth orbit (GEO) satellite while receivers are equipped on several unmanned aerial vehicles (UAVs). In the first stage, the reference range from the reference radar to the target is estimated by a quadratic function, and then the weighted least squares (WLS) solution of the target location is updated by substituting the range estimate back into it. In the second stage, we invoke the Taylor series approximation to further refine the target localization obtained by the first stage. It can be foreseen that the developed method is beneficial for scenarios with a limited number of radars, including engineering projects such as fire control, surveillance, and guidance, to support high-accuracy target localization. The simulation results show the superiority of the localization performance of the proposed method over other existing methods.

With the rapid development of cooperative detection technology, target fusion detection with regard of LEO satellites can be realized by means of their diverse observation configurations. However, the existing constant false alarm ratio (CFAR) detection research rarely involves the space-based target fusion detection theory. In this paper, a novel multi-source fusion detection method based on LEO satellites is presented. Firstly, the pre-compensation function is constructed by employing the range and Doppler history of the cell where the antenna beam center is pointed. As a result, not only is the Doppler band broadening problem caused by the high-speed movement of the satellite platform, but the Doppler frequency rate (DFR) offset issue resulted from different observation configurations are alleviated synchronously. Then, the theoretical upper and lower limits of DFR are designed to achieve the effective clutter suppression and the accurate target echo fusion. Finally, the CFAR detection threshold based on the exponential weighted likelihood ratio is derived, which effectively increases the contrast ratio between the target cell and other background cells, and thus to provide an effective multi-source fusion detection method for LEO-based satellite constellation. Simulation results verify the effectiveness of the proposed algorithm.

With the continuous improvement of radar resolution, distributed characteristics are presented with regard to maritime moving targets, which occupy multi-resolution cells in Synthetic Aperture Radar (SAR) image domain. However, the current constant false alarm rate (CFAR) detection algorithms rarely consider the impact of distributed characteristics on target detection performance, and thus the corresponding performance evaluation methods could not be applicable to this issue. In this paper, a multi-station fusion detection method for maritime moving target (MMT) is presented based on three-dimensional (3D) sliding window. Firstly, the multi-station echoes in Cartesian-Doppler frequency rate (DFR) domain are obtained under the simple-transmitting and multiple-receiving operation configuration, and the 3D sliding window is designed to achieve the optimal matching for a specified target with any moving direction in terms of its prior information, i.e. Radar Cross Section (RCS), radar resolution and target size. Then, the target cells within the sliding window are directly processed by means of the M/N criterion, and thus to avoid target detection performance loss caused by the artificial construction of extended detection statistics. Finally, a novel quantitative evaluation method in regards to a high-resolution radar is designed by mining the relationship between the target detection performance and the occupied cell number, which could greatly improve detection probability of distributed targets on the premise of a constant false alarm rate. The proposed algorithm does not need to design complex extended detection statistics, which provides a feasible way for robust detection and performance evaluation for a high-resolution radar. Simulation results verify the effectiveness of this research.

The orbital angular momentum (OAM) of a vortex electromagnetic wave (VEW) has gained attention as a newly explored information carrier. OAM modes provide vortex azimuth resolution, which is a new degree of freedom (DOF) in radar application. Due to the special characteristics of the vortex azimuth domain, VEW shares compound Doppler information of two-dimensional (2D) speed. This paper proposes a 2D target velocity estimation method for VEW radar. The Doppler effect of VEW is first analyzed. Based on the relativity of tangential speed and OAM mode, a pulse-by-pulse OAM mode-changing strategy is designed. Then, a modified Radon–Fourier transformation (RFT) is proposed to estimate the compound Doppler frequency while range migration is compensated. In addition, decoupling and ambiguity-solving procedures are applied to the compound Doppler frequency estimation to obtain tangential and radial speed estimations separately. According to the simulation analyses, the effectiveness of the proposed method is verified.

Space-time adaptive processing (STAP) performance in complex clutter environments often degrades due to the difficulty in obtaining sufficient independent and identically distributed (IID) training samples. Sparse Recovery (SR) STAP reduces IID sample requirements but faces challenges in parameter tuning and computational complexity. Deep learning (DL) STAP methods also lower IID sample needs and reduce online computation, but their poor interpretability limits reliability. In addition, both SR and DL STAP methods are prone to off-grid issues. To address these challenges, this paper proposes a multi-module deep convolutional neural network that combines data-driven and model-driven approaches to achieve fast and accurate clutter covariance matrix estimation under small-sample conditions. The network comprises four parts: a channel self-attention module, data modules, prior modules, and a hyperparameter module. Each module has a clear mathematical foundation and physical significance, enhancing the interpretability of the network. Meanwhile, the network leverages prior knowledge of clutter ridges to non-uniformly partition the spatial-Doppler profile, effectively mitigating the impact of off-grid issues. Both simulated and measured data demonstrate that the proposed method outperforms existing small-sample STAP methods in clutter suppression within non-homogeneous clutter environments, significantly reducing computational time.