Search Results (4)

Near-field multistatic radar imaging assumes that the transmitter, receiver, and target positions, as well as the receiver time references, are known exactly. In practice they are known only approximately: receiver positions and clocks carry small survey and synchronization errors, and target locations used to initialize or refine an image are themselves approximate. This paper develops a joint self-calibration framework that estimates small corrections to receiver positions, receiver clock biases, and target positions from the same bistatic echo delays used for imaging, and ties the correction directly to image sharpness rather than to parameter accuracy alone. We derive the linearized observation model relating delay residuals to these corrections, and give a regularized (maximum a posteriori) weighted least-squares estimator that explicitly separates measurement noise from prior parameter uncertainty. We characterize the identifiability of this estimator progressively, from a single anchor (the transmitter alone, which leaves an exact three-dimensional rotational null space) to two anchors (transmitter plus one additional point, which reduces the null space to a one-parameter rotation about a fixed axis) to three anchors (transmitter, one target, and one receiver, in general position, which removes the continuous ambiguity entirely). We additionally treat the dual problem of localizing an unknown transmitter from a small number of exactly known anchors—receivers, targets, or time samples of a single moving platform—and show that collinear or coplanar anchor geometries leave an exact, uncorrectable continuous or discrete ambiguity, respectively, regardless of how many such anchors are used, with the coplanar case notably invisible to a standard rank or conditioning check. We then reformulate the calibration objective directly in terms of coherent multistatic image sharpness, evaluated using the matched-filter score already used for image formation, and propose a two-stage algorithm: a coarse linear delay-residual solve followed by phase-coherent sharpness refinement. Numerical experiments verify the predicted identifiability transitions via the singular value spectrum of the linearized system, demonstrate quadratic convergence of the proposed estimator, verify the transmitter-localization ambiguity structure—including an exact mirror-twin solution for coplanar anchors, reproducing all range measurements to floating-point precision—and demonstrate the effect of self-calibration on a simulated multistatic image of an extended (eagle-shaped) target, including the incremental effect of bandwidth, aperture/frequency windowing, and CLEAN deconvolution on the recognizability of the resulting image, as well as on the resolvability of multiple simultaneous discrete targets (two instances of the same target). We relate this formulation to, and distinguish it from, the existing literature on time-of-arrival sensor network self-calibration and on joint target-localization/clock-bias estimation, which largely target single moving targets, anchor-free minimal-data solvability, or localization accuracy rather than multistatic image focus. Full article

GNSS-based navigation can become unreliable when signals are blocked or deliberately interfered with. For small UAV platforms operating in complex environments, this limitation motivates the exploration of alternative positioning strategies such as opportunistic navigation (OpNav). Achieving reliable high-precision positioning under a fully non-cooperative setting remains difficult in practice where no infrastructure information is available. This mode is defined by three key constraints: unknown transmitter locations, unknown environmental topology and strictly asynchronous clocks. To address this limitation, we develop a lightweight sensing and navigation framework designed for UAV platforms operating under strict hardware constraints. We model static scattering centers as environmental anchors, proving that these features restore system observability even with a single unknown emitter. To ensure real-time performance on lightweight flight controllers, a hierarchical two-stage solver is designed: Stage I derives a robust closed-form initial estimate via an algebraic differencing method that is agnostic to reflection orders; Stage II performs manifold refinement using a Clock-Null Projection (CNP) to attain the CRLB. This framework is confirmed through experiments in urban areas using commercial LTE signals. The results show that it can map unknown RF topologies with meter-level accuracy and keep navigating without prior infrastructure, offering a strong solution for UAV autonomy in environments where GNSS is unavailable. Full article

Recently, image haze removal has gained increasing attention in the field of image restoration. Data-driven and model-based methods are popular among researchers. The dark channel prior prevails in model-based methods, where the model parameters, atmospheric light, and transmittance are generally estimated through a block-based dark channel. This paper proposes a model-based approach with integrated pixel- and block-based dark channels for initial transmittance estimation. Additionally, we developed a two-stage guided image filtering process to refine the initial transmittance while utilizing the pixel-based dark channel to estimate atmospheric light. Our approach introduces two scaling factors for atmospheric light and initial transmittance, which are optimized using the Whale Optimization Algorithm. A CNN image regression model is employed to learn the mapping between hazy images and their corresponding optimized scaling factors, thus eliminating the need for ground-truth images. This makes our approach applicable in real-world scenarios. The proposed approach was validated using two datasets: an artificially generated image dataset, RESIDE, and a natural image dataset, KeDeMa. The results show that our approach outperforms four other dehazing methods, i.e., GCAN, RRO, RFDN, and Ka-Net. With the RESIDE dataset, our approach outperforms GCAN, RRO, RFD, and Ka-Net by 2.009 dB, 6.042 dB, 3.488 dB, and 8.975 dB, respectively, in terms of PSNR. With the KeDeMa dataset, our approach generally demonstrates superior visual quality to the four comparison methods. The results suggest that the proposed model-based approach may outperform data-driven methods. Full article

To address the concerns of contrast deterioration, detail loss, and color distortion in images produced under haze conditions in scenarios such as intelligent driving and remote sensing detection, an algorithm for image defogging that combines Retinex theory and the dark channel prior is proposed in this paper. The method involves building a two-stage optimization framework: in the first stage, global contrast enhancement is achieved by Retinex preprocessing, which effectively improves the detail information regarding the dark area and the accuracy of the transmittance map and atmospheric light intensity estimation; in the second stage, an a priori compensation model for the dark channel is constructed, and a depth-map-guided transmittance correction mechanism is introduced to obtain a refined transmittance map. At the same time, the atmospheric light intensity is accurately calculated by the Otsu algorithm and edge constraints, which effectively suppresses the halo artifacts and color deviation of the sky region in the dark channel a priori defogging algorithm. The experiments based on self-collected data and public datasets show that the algorithm in this paper presents better detail preservation ability (the visible edge ratio is minimally improved by 0.1305) and color reproduction (the saturated pixel ratio is reduced to about 0) in the subjective evaluation, and the average gradient ratio of the objective indexes reaches a maximum value of 3.8009, which is improved by 36–56% compared with the classical DCP and Tarel algorithms. The method provides a robust image defogging solution for computer vision systems under complex meteorological conditions. Full article