Synthetic aperture radar (SAR) is a kind of high-resolution imaging radar, which can work in any weather conditions and is often used for target positioning. Airborne SAR has the advantages of flexible scanning geometry and high spatial resolution compared with Spaceborne SAR, but the quality of SAR image and positioning accuracy is affected by air turbulence and mechanical vibration. In practical applications, it is vital to improve Airborne SAR’s geometric positioning accuracy without ground control points or other reference image information. When positioning a single SAR image with long-range, the Earth surface model is often used as an additional equation to solve the Range-Doppler (RD) model. However, the Earth surface equation is difficult to solve the target height accurately. This study proposed an Airborne SAR target positioning methodology based on a local linear approximation of the Earth surface equation. The proposed local Linear RD (L-RD) model establishes a linear constraint of the local elevation, and the iterative algorithm converges rapidly. To further improve the efficiency of geolocation, we propose a method to directly obtain the initial geolocation used for the iteration based on the geometric relationship. An iterative framework is also proposed to enhance positioning accuracy when DEM is available. The experimental results show that the proposed method can significantly improve the positioning accuracy and reliability of Airborne SAR positioning without control points compared to other methods.

Image matching between optical and SAR images remains challenging due to their different imaging principles. Despite recent developments in image matching methods for optical-to-SAR images, few have seen practical application success due to the significant geometric and radiometric differences between the two modalities. In this letter, we propose a new method called the Orientated Modality Independent Region Descriptor (OMIRD) to overcome these challenges. Our template-based approach effectively extracts consistent structural and textural features from both optical and SAR images. Additionally, we introduce a principal orientation selection method, using the Ratio of Exponentially Weighted Averages (ROEWA) and logarithm Histogram of Gradient (HOG) to identify the most meaningful feature orientations and further simplify the OMIRD descriptor. The experimental results indicate that the proposed OMIRD exhibits superior matching accuracy and robustness under various geometric distortions and non-linear grayscale variations.

Inclinometers are widely used in tilt measurement and their required accuracy is becoming ever higher. Most existing methods can effectively work only when the tilt is less than 60°, and the accuracy still can be improved. A redundant configuration of micro-electro mechanical system accelerometers is proposed in this paper and a least squares method and data processing normalization are used. A rigorous mathematical derivation is given. Simulation and experiment are used to verify its feasibility. The results of a Monte Carlo simulation, repeated 3000 times, and turntable reference experiments have shown that the tilt measure range can be expanded to 0°–90° by this method and that the measurement accuracy of θ can be improved by more than 10 times and the measurement accuracy of γ can be also improved effectively. The proposed method is proved to be effective and significant in practical application.

Aiming at the urgently required for miniaturization, low cost and high reliability in north seeking and attitude maintenance, a six-axis MEMS inertial measurement unit (IMU) and single-axis fiber optic gyro (FOG) composite measuring device is proposed, in which the six-axis MEMS IMU is installed in a conventional orthogonal configuration. While the single-axis FOG is mounted in parallel with the MEMS horizontal X-axis approximately. Based on this new combinational inertial measurement device, there are two functions can be achieved: North-seeking function, three-axis MEMS accelerometers and single-axis FOG are used to complete four-position north seeking and initial horizontal attitude calculation. Once the alignment is completed, the novel equipment constructs an attitude maintaining device and transfer the calculated attitude and orientation results to the six-axis MEMS-IMU. Concerning the low-speed working environment and changeable motion state of the equipment, the static state and uniform motion of the instrument can be automatically detected. The MEMS gyro error and attitude error are corrected by filter while in static and uniform-speed state, and maintain attitude tracking in other condition. A detailed calibration, alignment and attitude maintaining method of the new measuring device is introduced in this paper. The calibration of the turntable, the multi-position alignment of the turntable and the dynamic performance experimental results demonstrate that the novel equipment and method can effectively and feasibility achieve the user index requirements. The three-axis alignment accuracy is better than 1.5 degree (3.σ)and the 15-minute tilt maintaining accuracy is better than 2 degree (3σ).

With the advancement of remote sensing technologies, multimodal image matching has become increasingly important. Multimodal remote sensing images exhibit significant differences in radiometric and geometric properties due to their distinct imaging principles and being contaminated by various types of noise. Therefore, multimodal image matching remains a challenging problem. This paper proposes a novel descriptor called Shape-Adaptive Modality Independent Region Descriptor (SA-MIRD) to achieve robust matching across modalities by leveraging local structural self-similarities within the images. The proposed method first models the noise of each modality and uses a Local Polynomial Approximation-Intersection of Confidence Interval (LPA-ICI) to fit the local texture patterns. It then constructs the descriptor based on regional gradients extracted from local structural masks. Experimental results demonstrate the effectiveness of our approach in extracting structural features from multimodal images with good noise robustness, especially in regions with limited texture, such as mountainous and island areas. The proposed descriptor achieves high accuracy and robustness in multimodal image matching.