Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

State spaces are, in the most general sense, sets of entities that contain information. Examples include states of dynamical systems, processes of observations, or possible worlds. We use domain theory to describe the structure of positive and negative information in state spaces. We present examples ranging from the space of trajectories of a dynamical system, over Dunn’s aboutness interpretation of fde, to the space of open sets of a spectral space. We show that these information structures induce so-called hype models which were recently developed by Leitgeb (2019). Conversely, we prove a representation theorem: roughly, hype models can be represented as induced by an information structure. Thus, the well-behaved logic hype is a sound and complete logic for reasoning about information in state spaces.

As application of this framework, we investigate information fusion. We motivate two kinds of fusion. We define a groundedness and a separation property that allow a hype model to be closed under the two kinds of fusion. This involves a Dedekind–MacNeille completion and a fiber-space like construction. The proof-techniques come from pointless topology and universal algebra.

X-ray Pulsar Navigation (XPNAV) uses the Time Difference of Arrival (TDOA) of the pulsar signal between the spacecraft and Solar System Barycentre (SSB) to determine position. In this paper, a novel method to improve the performance of XPNAV via exploiting the pulsar position vector is proposed. First, the field of view of the collimator is utilised to find the pulsar orientation direction. Then, a searching strategy based on the modified Powell method under given coordinate frames is proposed. We also mathematically prove the existence of the extreme value of the searching strategy. Subsequently, an observation model based on the pulsar radiation vector is presented and applied to formulate the observation function together with pulsar time transfer function. Finally, an Adaptive Divided Difference Filter (ADDF) algorithm is introduced to iteratively estimate the position and velocity of the spacecraft. Numerical simulations show that the vector searching method is feasible and the pulsar radiation direction can improve the navigation performance by 75%. The simulation results also show that the ADDF performs better than Unscented Kalman Filtering (UKF) and DDF in position estimation.

To obtain accurate navigation results with respect to Earth simultaneously with those with respect to the target for an interplanetary probe to approach the target planet, this paper proposes a Radio/Optical integrated navigation method based on ephemeris correction, which deeply affects the fusion accuracy. In this paper, the model of the ephemeris error is established, and taking the analytical solution of the ephemeris uncertainty as measurement, the target ephemeris error and its covariance are estimated by Kalman filter and fed back to modify the force models. By correcting the target ephemeris and using information fusion, the Radio/Optical integrated navigation prevents the ephemeris uncertainty polluting the fusion accuracy, and efficiently combines the radio and optical navigation results. The results show the influence of the ephemeris error can be removed, and the Radio/Optical integrated navigation is capable of providing accurate navigation results with respect to Earth and the target. The results demonstrate the proposed method yields an accuracy superior to the conventional method, which proves its effectiveness.

For a specific query merging the returned results from multiple search engines, in theform of a metasearch aggregation, can provide significant improvement in the quality ofrelevant documents. This paper suggests a minimax linear programming (LP) formulation forfusion of multiple search engines results. The paper proposes a weighting method toinclude the importance weights of the underlying search engines. This is a two-phaseapproach which in the first phase a new method for computing the importance weights of thesearch engines is introduced and in the second stage a minimax LP model for findingrelevant search engines results is formulated. To evaluate the retrieval effectiveness ofthe suggested method, the 50 queries of the 2002 TREC Web track were utilized andsubmitted to three popular Web search engines called Ask, Bing and Google. The returnedresults were aggregated using two exiting approaches, three high-performance commercialWeb metasearch engines and our proposed technique. The efficiency of the generated listswas measured using TREC-Style Average Precision (TSAP). The new findings demonstrate thatthe suggested model improved the quality of merging considerably.

This paper introduces an architecture for aggregation of output from different diagnostic tools. The diagnostic fusion tool deals with conflict resolution, where diagnostic tools disagree; temporal information discord, where the estimate of different tools is separated in time; differences in information updates, where the classifiers are updated at different rates; fault coverage discrepancies; and integration of a priori performance specifications. To this end, a hierarchical weight manipulation approach is introduced which creates and successively refines a fused output. The performance of the fusion tool is evaluated throughout its design. This allows impact assessment of adding heuristics and enables early tuning of parameters. Results obtained from diagnosing on-board faults from aircraft engines are shown which demonstrate the fusion tool's operation.