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.

The speed and direction of a missile shifts sharply in the dive phase, making the azimuth frequency modulation (FM) rate change with the azimuthal position, leading to azimuth ambiguities and image distortion. To solve this problem, a modified nonlinear chirp scaling (NLCS) algorithm was adopted to compensate for the azimuth FM rate. First, the geometric configuration and echo signal model of the spaceborne missile bistatic synthetic aperture radar (SAR) were built, and then the Doppler frequency correction was performed, and the 2-D spectrum of the signal was derived by the method of series reversion. Next, range migration correction and range compression were finished in the 2-D frequency domain. Following this, a modified NLCS algorithm was proposed to solve the space variance of Doppler phase problem. After compensating for the azimuth FM rate, the azimuth compression focusing was completed and the imaging result was obtained. Finally, by comparing the calculation amount, imaging effect, and performance index with the traditional NLCS algorithm, it can be concluded that the algorithm reduced the calculation amount by 1.0128 × 10⁸ floating point operations per second (FLOPs) compared with the traditional NLCS algorithm, and the azimuth focusing effect of the edge point was greatly improved. Its resolution, peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR) were improved by 0.87 m, 3.32 dB, and 1.79 dB, respectively, which proved the effectiveness and feasibility of this method.

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.

Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space–time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne/airborne radars.