Multi-station passive localization algorithms based on hybrid Time Difference of Arrival (TDOA) and Angle of Arrival (AOA) have been thoroughly studied. But it usually requires each station to obtain the AOA of the source and relies on the precise station position. These conditions are generally not satisfied in practical applications. In addition, the geometry between the source and these stations also affects the localization accuracy. This paper studies the passive source localization problem based on multi-station TDOA and single-station AOA measurements. First, we propose a closed-form solution source localization method based on multi-station TDOA and single-station AOA measurements, which requires only the reference station used to calculate TDOA to be able to observe the AOA measurements of the source. It solves the problem of hybrid localization of different numbers of TDOA and AOA measurements. Theoretical analysis proves that the performance of the proposed method can reach the Cramer-Rao Lower Bound (CRLB) under small noise conditions. Then, we model the relationship between the position of each station relative to the source and the localization accuracy. Through the D-optimality criterion, we obtain the optimal geometry between multiple stations and the source Simulation results confirm the validity of these theories.

Images acquired by airborne infrared search and track (IRST) systems are often characterized by nonuniform noise. In this paper, a scene-based nonuniformity correction method for infrared focal-plane arrays (FPAs) is proposed based on the constant statistics of the received radiation ratios of adjacent pixels. The gain of each pixel is computed recursively based on the ratios between adjacent pixels, which are estimated through a median operation. Then, an elaborate mathematical model describing the error propagation, derived from random noise and the recursive calculation procedure, is established. The proposed method maintains the characteristics of traditional methods in calibrating the whole electro-optics chain, in compensating for temporal drifts, and in not preserving the radiometric accuracy of the system. Moreover, the proposed method is robust since the frame number is the only variant, and is suitable for real-time applications owing to its low computational complexity and simplicity of implementation. The experimental results, on different scenes from a proof-of-concept point target detection system with a long-wave Sofradir FPA, demonstrate the compelling performance of the proposed method.

Aerial infrared point target detection under nonstationary background clutter is a crucial yet challenging issue in the field of remote sensing. This paper presents a novel omnidirectional multiscale morphological method for aerial point target detection based on a dual-band model. Considering that the clutter noise conforms to the Gaussian distribution, the single-band detection model under the Neyman-Pearson (NP) criterion is established first, and then the optimal fused probability of detection under the dual-band model is deduced according to the And fusion rule. Next, the omnidirectional multiscale morphological Top-hat algorithm is proposed to extract all the possible targets distributing in every direction, and the local difference criterion is employed to eliminate the residual background edges further. The dynamic threshold-to-noise ratio (TNR) is adjusted to obtain the optimal probability of detection under the constant false alarm rate (CFAR) criterion. Finally, the dim point target is extracted after dual-band data correlation. The experimental result demonstrates that the proposed method achieves a high probability of detection and performs well with respect to suppressing complex background when compared with common algorithms. In addition, it also has the advantage of low complexity and easy implementation in real-time systems.

In order to improve the image quality of the aerial optoelectronic sensor over a wide range of temperature changes, high thermal adaptability of the primary mirror as the critical components is considered. Integrated optomechanical analysis and optimization for mounting primary mirrors are carried out. The mirror surface shape error caused by uniform temperature decrease was treated as the objective function, and the fundamental frequency of the mirror assembly and the surface shape error caused by gravity parallel or vertical to the optical axis are taken as the constraints. A detailed size optimization is conducted to optimize its dimension parameters. Sensitivities of the optical system performance with respect to the size parameters are further evaluated. The configuration of the primary mirror and the flexure are obtained. The simulated optimization results show that the size parameters differently affect the optical performance and which factors are the key. The mirror surface shape error under 30 °C uniform temperature decrease effectively decreased from 26.5 nm to 11.6 nm, despite the weight of the primary mirror assembly increases by 0.3 kg. Compared to the initial design, the value of the system’s modulation transfer function (0° field angle) is improved from 0.15 to 0.21. Namely, the optical performance of the camera under thermal load has been enhanced and thermal adaptability of the primary mirror has been obviously reinforced after optimization. Based on the optimized results, a prototype of the primary mirror assembly is manufactured and assembled. A ground thermal test was conducted to verify difference in imaging quality at room and low temperature, respectively. The image quality of the camera meets the requirements of the index despite degrading.

Focal-plane arrays (FPAs) are often interfered by heavy fixed-pattern noise, which severely degrades the detection rate and increases the false alarms in airborne point target detection systems. Thus, high-precision nonuniformity correction is an essential preprocessing step. In this paper, a new nonuniformity correction method is proposed based on a staircase scene. This correction method can compensate for the nonlinear response of the detector and calibrate the entire optical system with computational efficiency and implementation simplicity. Then, a proof-of-concept point target detection system is established with a long-wave Sofradir FPA. Finally, the local standard deviation of the corrected image and the signal-to-clutter ratio of the Airy disk of a Boeing B738 are measured to evaluate the performance of the proposed nonuniformity correction method. Our experimental results demonstrate that the proposed correction method achieves high-quality corrections.

The research on optical imaging characteristics of infrared dim point targets in the presence of nonstationary cloud clutter and random noise is necessary for target detection. We analyze the energy concentration of point targets that are less than $3\times 3$ pixels in size and deduce a simulation model of the point target imaging process. Then we adopt omnidirectional multiscale structural elements to detect all the possible targets distributing in every direction. The adaptive threshold and the energy concentration criterion are employed to eliminate false alarms. Finally, the trajectory of point targets is obtained after the low-order recursive correlation. The results show that the detection probability of the proposed method reaches 99.8% with 0.2% false alarm probability. It demonstrates that the proposed method has a good performance to suppress complex background and random noise. Also, it has the advantage of low complexity and easy implementation in a real-time system.