In general, target localization in distributed multiple-input multiple-output (MIMO) radar systems requires synchronization among the sensors, which may not be feasible when the sensors are widely separated. In this paper, the target localization problem is addressed under asynchronous distributed MIMO radar systems in the presence of clock and frequency offsets with the aid of a cooperative target, whose position and velocity are known with measurement errors. Specifically, the influence of the cooperative target position and velocity errors on the unknown target localization accuracy is analyzed through the Cramer-Rao lower bound (CRLB) derivation firstly. Then two methods, namely, the offset estimation based localization method and the offset elimination based localization method, are proposed to estimate the unknown target location. The former estimates the clock and frequency offsets firstly and then locates the unknown target based on the offset estimates, and the latter achieves the unknown target location estimate based on the differential measurements of the two targets. Through the theoretical derivation and numerical simulations, the two proposed methods are demonstrated to be approximately unbiased and can attain the CRLB under mild noise. Moreover, the simulation results show that the proposed methods enjoy higher localization accuracy than the state-of-the-art algorithms.

High-resolution remote sensing image object detection plays an increasingly important role in image processing and interpretation. The application of region-based convolutional neural network (R-CNN) greatly enhances the performance of object detection. However, the attributes of remote sensing images such as overlarge image size, similar background, disequilibrium distribution of categories make this task more challenging. The previous works have focused on extracting multi-scale features of region proposals, often ignoring the quality of region of interest (ROI). In this work, we proposed a patch-based three-stage aggregation network (PTAN) for object detection in high-resolution remote sensing images. It consists of a three-stage cascade structure that sequentially improves the quality of candidate regions by increasing the IoU threshold stage by stage, and adopts a resampling strategy to obtain sufficient region proposals. At the same time, we also proposed patch-based strategy and applied it to the framework during training and inference. Ablation experiments and comprehensive evaluations on a communal remote sensing image object detection dataset DOTA demonstrate the effectiveness and robustness of the proposed framework, which obtained a mean average precision (mAP) value of 0.7958 on validation dataset and a front-rank mAP of 0.7858 on testing dataset. On another remote sensing image object detection dataset NWPU VHR-10, the proposed PTAN obtained a mAP value of 0.9187, outperforming other five object detectors.

Foot-mounted pedestrian indoor positioning is very suitable for emergency response missions such as search and rescue missions. With some aiding information, such as UWB based ranging measurements, the inertial generated trajectories can be calibrated. However, in situations when the ranging is not available for a long duration, the trajectories may result in sudden changes and large errors. This paper propose to adopt the particle filter based smoothing techniques, including the forward-backward smoothing and backward simulation, to cope with such situations. Both the results from simulation and real-scenario experiments have demonstrated the effectiveness of the proposed method in avoiding sudden changes in trajectories and improving average positioning accuracy.