RESUMO
In view of the complexity of port ship supervision and the influence of external factors such as electromagnetic interference in harbor-border inspection, an efficient system combining an unmanned aerial vehicle (UAV) and visible light positioning (VLP) is proposed for locating maritime targets. In this system, a rotatable receiver with five photodetectors (PDs) installed obliquely on UAV is designed for expanding the positioning range and allowing a lower flight altitude. On this basis, we propose the Chan-Taylor (CT) method based on time difference of arrival (TDOA) for target positioning. First, the localization problem is reformulated as a weighted least squares (WLS) problem and provides a good initial estimate via the two-step WLS (TWLS) method. Then, based on Taylor expansion of TDOA equations, estimated error is calculated using the initial estimate, which can correct the estimated position of the target iteratively. To offset the error, weighted centroid CT (WCCT) is proposed by endowing different weights based on error difference to estimated results. For further improving accuracy, a restricted-region fingerprinting positioning based on CT (CT-RFP) is proposed. In restricted area determined by CT, a certain number of fingerprints is generated based on received signal strength (RSS) for matching. Simulation results show that CT is significantly improved over the previous methods. Compared with TWLS, the accuracy of CT is improved by 49.71%. For WCCT, the maximum error is reduced from 8.65 to 6.91 cm, which effectively reduces the influence of error. Moreover, CT-RFP can achieve an accuracy within millimeter level via the appropriate number of fingerprints and ensemble runs of CT, even at high noise levels.
RESUMO
The unmanned aerial vehicle (UAV) offers unique advantages of autonomous flight capability and small coefficient of risk, and is increasingly being used in harbor-border inspection to ensure security and orderly operation of harbors. In response to the influence of external factors such as electromagnetic interference in harbor-border inspection, this paper utilizes UAV and visible light communication (VLC) to build an efficient system to track maritime targets near the harbor reliably. In a VLC scenario, a geometrical equation for transmitter positioning is first proposed based on the received signal strength of the optical signal emitted by the target. On this basis, linear iterative positioning (LIP) using first-order Taylor expansion is proposed to realize online beam tracking. Furthermore, quadratic approximative iterative positioning (QAIP), a more precise approximation of the geometrical equation, is proposed based on second-order Taylor expansion. Simulation results show that the proposed algorithms can track targets effectively, and QAIP can achieve higher accuracy with no noise or high signal-to-noise ratio. In addition, compared with the geometrical solution, LIP and QAIP have faster computing speeds and fixed overheads.
RESUMO
Fast camera fingerprint search is an important issue for source camera identification in real-world applications. So far there has been little work done in this area. In this paper, we propose a novel fast search algorithm. We use global information derived from the relationship between the query fingerprint/digest and the reference fingerprints/digests in the database to guide fast search. This information can provide more accurate and robust clues for the selection of candidate matching database fingerprints. Because the quality of query fingerprints may degrade or vary in realistic applications, the construction of robust search clues is significant. To speed up the search process, we adopt a lookup table that is built on the separate-chaining hash table. The proposed algorithm has been tested using query images from real-world photos. Experiments demonstrate that our algorithm can well adapt to query fingerprints with different quality. It can achieve higher detection rates with lower computational cost than the traditional brute-force search algorithm and a pioneering fast search algorithm in literature.
