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When applying the widely-used Newton-Raphson (N-R) method to the integrated solution of the hybrid power-water flow problem of the power-water integrated energy system (PW-IES), it is found that there are numerical differences among Jacobian elements, which may yield an ill-conditioned Jacobian matrix and even cause convergence problem. Therefore, this letter proposes to per-unitize the water distribution network (WDN) to reduce the numerical differences and thus to improve the Jacobian matrix conditioning and the integrated N-R solution to the hybrid power-water flow problem.

The “masking effect” of unresolved targets always results in missed detection and inaccurate parameter estimation. A feasible approach to separate the unresolved targets is improving the range resolution by increasing the signal bandwidth. In this paper, we propose a novel scheme to detect and measure the unresolved targets for the phased array radar with stepped-frequency waveform, which provides a large synthetic bandwidth. We establish the signal model of the stepped-frequency pulse train and review the conventional 1-D High Range Resolution Profile (HRRP). Since the 1-D HRRP of each element fails to integrate the spatial domain, we develop a 2-D HRRP where the target echo can be effectively integrated both in time and spatial domains. Specifically, the 2-D HRRP is generated by the range-angle beamforming technique. In order to address the range shift in the 2-D HRRP caused by target motion, we design a short-long stepped-frequency pulse train as the transmit waveform, which contains a short pulse subtrain with a small pulse repetition interval (PRI) and a long pulse subtrain with a large PRI. The proposed scheme includes two parts of beamforming detection and parameter estimation with range shift elimination. Based on two 2-D HRRPs generated by short and long pulse subtrains, each target can be successfully detected by beamforming detection and target's range, angle and velocity can be accurately estimated by parameter estimation with range shift elimination. Simulation results demonstrate the effectiveness of the proposed scheme.

The diversity of magnetic field information obtained in marine geomagnetic measurements is of great relevance to defining detailed features of the targets. In this article, a solution algorithm based on the combination of scalar and vector fields is proposed. The magnetic direction of the geomagnetic field was obtained by repeatedly applying bias fields, and the multiparameter integrated measurement is realized based on the geometric relationship of the geomagnetic parameters. To make an instrument feasible for undersea measurement, a miniaturized multiparameter magnetometer was constructed within the limited space of a pressure tank, using an Overhauser scalar sensor and a spherical coil for generating bias fields. Test platforms were built to evaluate the scalar sensor and spherical coil. Results from a nonmagnetic laboratory showed that the expanded uncertainty of the scalar sensor was 0.11 nT, whereas the magnetic field generated by the spherical coil had 97% uniformity and completely engulfed the scalar sensor. Moreover, field experiments indicated that the standard deviation of prototype instrument in magnetic direction (3 $^{\prime \prime }$) was close to that of declination–inclination magnetometer, whereas its standard deviation in Z-component of 0.24 nT was better than that of a commercial fluxgate magnetometer. It also had markedly better measurement stability, with no temperature drift. [ABSTRACT FROM AUTHOR]

In aeromagnetic surveys, poor aircraft heading and weather may frequently cause a classical optically-pumped sensor to enter into or close to its dead zone, which results in unavoidable outliers that seriously reduce aeromagnetic compensation. To address these problems, a method to identify rapidly and pre-cull magnetic outliers based on the gray system theory is proposed to reduce their negative influence during the estimation of coefficients and target detection robustness. By constructing a gray region of aeromagnetic data and then checking whether the data at the end points of the region are normal, aeromagnetic outliers can be culled. The simulation results show that even if the outlier rate is increased to 20%, the average of the correct culling rate of the proposed method can still reach 99.67%, at which the culling effect is highly robust. We constructed an experimental survey platform and conducted a flight test. The results show that the improvement ratio of the proposed method can reach 4.36, which is 10.38 times higher than the conventional method. [ABSTRACT FROM AUTHOR]

Earth magnetic field sensors are used to detect the geomagnetic field and are critical for research in the geosciences and in prospecting for mineral resources. Today, the Overhauser sensor is the most accurate scalar sensor among widely applied geomagnetic field sensors. However, the Overhauser sensor shows severe orientation sensitivity due to its solenoid induction coil. Hence, in mobile applications that use Overhauser sensors, the accuracy of geomagnetic field data is decreased because of this orientation sensitivity. This paper proposes a method of suppressing the orientation sensitivity of the Overhauser sensor from the perspectives of the sensor structure, signal conditioning circuit, and measurement algorithm. To address the problem of no signal output from the sensor in a certain orientation, the parameters of the solenoid coil are optimized. Moreover, we designed a frequency adaptive high-accuracy signal extraction method based on a closed-loop feedback strategy, to decrease the measurement error contributed by the signal conditioning circuit and frequency measurement algorithm. Finally, we implement a prototype device using the proposed method with the commercial instruments to characterize the performance of our prototype, and test the orientation sensitivity of the prototype in the standard magnetic metering station. Experimental results demonstrate that the proposed sensor can achieve high-accuracy measurement capabilities comparable to those of commercial instruments in any orientation, indicating that its orientation sensitivity is efficiently suppressed. [ABSTRACT FROM AUTHOR]

The initial orientation error of a coil vector magnetometer directly determines the measurement accuracy of the magnetic field direction. The traditional initial orientation method requires auxiliary manually operated instruments. To solve this problem, we propose an electrical orientation method based on applying bias fields. To analyze quantitatively the orientation error of the proposed method, a physical model for the coil orientation indicator and measurement errors of the magnetic direction was developed that exploits measurements of the deflected field obtained after applying two oppositely directed bias fields. The simulation results show that, when the difference indicator is 0.7 nT, the measurement error of the magnetic direction is less than $9\,''$. Moreover, when the bias field strength is greater than 30% of the geomagnetic field strength during the orientation operation, the influence of the change in strength of the bias field on the measurement error of the magnetic direction diminishes significantly. The indirect effect of the measurement error of the total-field sensor on the measurement accuracy of the magnetic direction was also analyzed. To verify this method, we designed an experimental setup and conducted a comparative experiment. The results show that as the difference indicator increases, the measurement error of the magnetic direction increases accordingly. When this indicator is adjusted to about 0.7 nT, the measurement error of the magnetic direction is less than $11.16''$ , which is in agreement with theoretical results. [ABSTRACT FROM AUTHOR]

Using a prior data to solve for the characteristic parameters of a moving magnetic target is crucial in its detection. For this purpose, we developed a real-time detection method using distributed scalar sensor networks based on a hybrid algorithm combining particle swarm optimization and the Gauss-Newton method. The magnetic anomaly fitting model of the target is established using the magnetic-dipole-moment principle. The PSO’s insensitivity to the initial solution is used to obtain a rough solution of the coefficients of the fitting, and then a more accurate solution is developed using the Gauss-Newton method, exploiting its advantage in high precision. Furthermore, to evaluate the proposed method, we built a test platform. The detection results show that the goodness-of-fit reaches 0.9565 and only requires 3.4 s; in the 16 m horizontal distance, the calculated trajectory overlaps significantly the actual trajectory, and errors in position and speed are around 10% and 4.25%, respectively. [ABSTRACT FROM AUTHOR]

The proton precession magnetometer (PPM) is always employed for geomagnetic observation, magnetic anomaly detection, etc., while the tuning performance of the PPM determines the signal-to-noise ratio (SNR) of the free induction decay (FID) signal output from the sensing coil. A tuned circuit was always used for the PPM sensing coil because of its narrow-band characteristic and high sensitivity. However, the FID signals might be distorted by the narrow bandwidth, which influences the PPM’s measurement accuracy for the geomagnetic field. To address this problem, we propose a tuning-matching framework consisting of an adjustable tuning capacitor network (ATCN) in parallel with a $\pi $ -type LC band-pass filter (LCBF), dubbed ATCN-LCBF. The equivalent circuit of the ATCN-LCBF are investigated and the corresponding electrical parameters are theoretically calculated. Consequently, the proposed ATCN-LCBF is conducted with a bandwidth of 3500 Hz, a pass-band gain of 5 dB, and an average noise floor of 1.67 nV/Hz1/2. The field experimental results demonstrate that the ATCN-LCBF can not only enhance the strength of the FID signal but also suppress the external noises, improving the signal detection performance of the PPM in a scenario with strong interference. [ABSTRACT FROM AUTHOR]