Hui Cao

Due to the high-precision positioning demands of automated guided vehicles in electromagnetically sensitive and wireless-restricted fully-automatic factories, a fully passive fusion positioning method employing fiber Bragg grating array sensors is developed in this paper. First, the grating array sensing fiber signal is parametrically modeled, and a feature-matching algorithm is applied to link physical locations with signal waveform features, providing high-fidelity data for dynamic positioning. Then, leveraging the principle of real-time velocity calculation within fiber partitions, a particle filter-based multi-fiber partition information fusion and state recursion framework is proposed. This framework iteratively predicts and compensates for target motion within the fiber sensing blind zone, overcoming the spatial resolution limitations of fiber sensing systems and significantly improving trajectory estimation accuracy from meter-level to centimeter-level. Finally, by taking advantage of real-time iteration across multiple sensing fiber partitions, a dynamic feedback correction mechanism is constructed. This mechanism corrects the predicted state in real time using interval observation data, effectively constraining accumulated errors within the physical intervals of adjacent feature points, addressing the error accumulation issue common in traditional partitioned velocity positioning methods. Experimental results demonstrate that by combining the grating array fiber multi-partitions sensing method with the feature matching particle filter positioning algorithm, the dynamic average positioning error can be less than 2 cm, and it has the ability to determine the parking position with high precision. This provides a reliable technical solution for centimeter-level positioning of automated guided vehicles in fully-automatic factories.

We investigate the problem of detecting the distributed targets buried in the Gaussian noise whose covariance matrix is unknown when signal mismatch occurs. The idea is to add a fictitious signal under the null hypothesis of the origin detection problem so that when signal mismatch occurs, the fictitious signal captures the mismatched signals, thus making the null hypothesis more plausible. More precisely, the fictitious signal is modeled as a Gaussian component with a covariance matrix of a stochastic factor multiplied by a rank-one matrix. The generalized likelihood ratio test (GLRT) is employed to address the modification detection problem. We present an exhaustive derivation of the detector and prove that it possesses the constant false alarm rate (CFAR) property. The performance analysis demonstrates the effectiveness of the proposed detector. When the SNR is 23 dB, as generalized cosine squared decreases from 1 to 0.83, the detection probability of the proposed GLRT-SL drops to 0.65, exhibiting the fastest decline compared to the G-ABORT-HE, which falls to 0.98, and the GW-ABORT-HE, which decreases to 0.85.

Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering the limited energy budget of UAVs and the existence of multiple jammers, we introduce a simultaneous wireless information and power transfer (SWIPT) technology and study the problems of joint-trajectory planning, time, and power allocation to increase communication performance. Specifically, the network includes multiple UAVs, source nodes (SNs), destination nodes (DNs), and jammers. We assume that the UAVs need to communicate with DNs, the SNs use the SWIPT technology to transmit wireless energy and information to UAVs, and the jammers can interfere with the channel from UAVs to DNs. In this network, our target was to maximize the throughput of DNs by optimizing the UAV’s trajectory, time, and power allocation under the constraints of jammers and the actual motion of UAVs (including UAV energy budget, maximum speed, and anti-collision constraints). Since the formulated problem was non-convex and difficult to solve directly, we first decomposed the original problem into three subproblems. We then solved the subproblems by applying a successive convex optimization technology and a slack variables method. Finally, an efficient joint optimization algorithm was proposed to obtain a sub-optimal solution by using a block coordinate descent method. Simulation results indicated that the proposed algorithm has better performance than the four baseline schemes.