Ocean Surface Current Measurement Using Shipborne HF Radar: Model and Analysis

Sea clutter has a major impact on the detection performance of a shipborne high-frequency surface wave radar (HFSWR) system. Due to the platform motion of shipborne HFSWR, the Doppler spectrum of the first-order sea clutter suffers from some broadening so that the targets submerged in this broadening Doppler spectrum can be hardly detected. In this letter, an improved oblique projection (IOP) method, combining the oblique projection (OP) algorithm and the method of sea clutter suppression in the Doppler domain, is proposed to suppress sea clutter in both Doppler domain and spatial domain for shipborne HFSWR. Compared with the OP and the orthogonal weighting algorithms, the proposed IOP algorithm is shown to give far superior suppression results in the Doppler domain and can achieve better azimuth estimation results based on real data.

Shipborne high-frequency surface wave radar (HFSWR) plays an important role in vessel target surveillance. However, additional modulation on the echo signal introduced by the platform motion, including the forward movement and six-degree-of-freedom (6-DOF) motion may further aggravate the inaccuracy of target direction of arrival (DOA) estimation and severely limits its detection ability. This paper aims to establish the platform-motion model and mathematically analyze the influence of platform motion on target DOA estimation. Considering the forward movement, 6-DOF motion, and the installed irregular radar array, the formula affecting the target azimuth is derived and the new time-varying steering vector is determined. Simulation results validate the theoretical analysis and the derived formula. In addition, forward movement and yaw motion have the most crucial effect on DOA estimation because these two motions change the platform location and detection area.

In this article, a new target bearing angle estimation method for ship-borne high-frequency surface-wave radar (HFSWR) is presented. This new method is designed to give bearing angle estimation of ship targets. It relies on platform motion instead of antenna aperture to acquire bearing angle resolving capability, and hence can be applied to ship-borne HFSWRs that only have very small antenna arrays, and gives small vessels over-the-horizon (OTH) detection capability. This new method requires little modification to existing HFSWR hardware, and only a small amount of additional memory and computing power is needed. Both theoretic analysis and implementation details are given in this article. The new method is tested using real-measured ship-borne HFSWR radar data. The test result proved the effectiveness of the new method.

Sea clutter has a serious influence on the detection performance of a shipborne high‐frequency surface wave radar (HFSWR) system, especially when the target is submerged in the spread Doppler spectrum of the first‐order sea clutter. A prediction cancellation (PC) method based on the radial basis function neural network (RBFNN) is put forward to suppress the first‐order sea clutter in time domain for shipborne HFSWR. Using the RBFNN trained by the averaged sea clutter data from the range cells surrounding the range cell of interest, the PC algorithm is implemented in the interested range cell. Compared with the two‐dimensional fast Fourier transformation plus digital beam forming algorithm, the proposed PC algorithm is shown to give far superior sea clutter suppression results based on real data.

This letter proposes using the 2-D multiple-signal classification (MUSIC) based on sparse recovery (SR) to improve the target-detection capability of high-frequency surface wave radar (HFSWR). Usually, for wide-beam HFSWRs, target detection is first conducted in the range-Doppler spectrum, and bearings are then estimated by superresolution methods such as MUSIC. Unfortunately, the conventional cascaded method can easily result in unfavorable deterioration of multitarget detection when different target signals tend to become mixed in the Doppler spectrum. Moreover, sea clutter is an unwanted signal that frequently masks target signals. To enhance the detection of multiple targets and targets embedded in sea clutter, spatial-temporal joint estimation has been proposed. However, because of the lack of spatial-temporal snapshots caused by the nonstationarity of target signals, the efficiency of the estimator cannot be guaranteed. To overcome this shortcoming, multiple-measurement-vector-based SR, which has been used to solve many under-sampling problems in the past ten years, is adopted. Our approach can effectively detect a target embedded in sea clutter as well as multiple adjacent targets and distinguish them from each other. Results obtained using real data with opportunistic targets validate our approach. Therefore, the proposed 2-D SR-MUSIC approach improves target detection and outperforms conventional cascaded methods.

This study proposes a compensation strategy to deal with both current and voltage measurement errors in the three-phase PWM rectifier system. The DC offset and scaling errors in the voltage and current measurements cause the injection of undesired DC and unbalanced currents into the three-phase input current and subsequently lead to voltage ripple at the DC output voltage. This paper proposes a compensation scheme for current measurement error where the DC offset and scaling errors in the current measurement are estimated from the characteristic of the DC output voltage ripple combining with simple band-pass and low-pass filters. Meanwhile, an advanced current controller designed with a proportional integral plus two resonant controllers tuned at the fundamental grid frequency (ω <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> ) and 2ω <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> in the synchronous (d-q) reference frame is suggested to reject the impact of the DC offset and scaling errors in the voltage measurement. The proposed compensation method is developed without the need of extra hardware circuit, sensor, or precise information of system parameters so that it can be considered as more cost effective and robust solution. The effectiveness of the proposed solution is verified by experimental results.

The servo system of a surface permanent magnet synchronous motor is operated by current sensors to control the motor torque. However, a current sensor has often offset and gain deviation caused by age-related deterioration and thermal variation. These errors lead to d - q axis current ripple and torque ripple, degrading the performance of the servo system. In order to overcome these problems, a new estimation and compensation method for current measurement errors by using a current simulator is proposed. The method aims to decrease torque ripple caused by both offset and gain deviation during current measurement. Experimental results confirm that the proposed method satisfactorily suppresses current ripple and torque ripple caused by current measurement error.

A high-frequency surface-wave radar (HFSWR) ship tracking algorithm’s performance is significantly affected by the dynamics of ships, in which track fragmentation can be frequently observed. However, it is still unclear about in which scenarios the dynamics of ships sabotages the tracking performance. In this paper, an evolutionary-based vulnerability detection method is proposed to automatically collect scenarios of different ship motion dynamics that can cause quantitative failures in a HFSWR ship tracking algorithm. Firstly, a grammar-based scenario model which can describe multiple types of temporal relationships and generate autonomous motion of any number of ships with comparatively low-dimension data is proposed. Secondly, an encoding scheme of scenario is proposed and corresponding grammar-guided genetic programming (GGGP) algorithm is designed to evolve scenarios that can sabotages the tracking performance. Results show the effectiveness of this method in evolving and collecting scenarios that can cause more serious track fragmentation in the tracking results, with insights into the vulnerability of ship tracking algorithm provided.

The errors in the current radial velocity measurements are examined using Bartlett beamforming and Multiple Signal Classification (MUSIC) direction-finding algorithms with a linear phased array antenna system. A variety of radar and environmental parameters are examined. Suggestions for the optimal choice of operating parameters are proposed. The MUSIC algorithm has shown promising performance in current measurement when beamforming is used to first establish the maximum current velocity. Comparisons of radar field data and current meter measurements show RMS radial velocity differences in magnitude of 7.44 and 6.64 cm s−1 for the Bartlett beamforming and MUSIC–Bartlett algorithms, respectively. The results indicate that there are advantages to using a MUSIC–Bartlett approach in operational applications.

This paper deals with the online parameter estimation of squirrel-cage induction machines using the Gradient Descent Method and coupled stator and rotor flux observers. Stator resistance, rotor resistance and mutual inductance can be estimated simultaneously in real time. For a non-ambiguous parameter tracking, an additional voltage is superimposed. The influence of inverter nonlinearity, current and speed measurement errors is investigated. The presented approach is applicable to a standard industrial drive, including current measurement and motor speed feedback. The concept is verified by experimental tests for open loop V/f control as well as for field-oriented control.

This paper is concerned with the current ripples which originate from the current measurement errors of a doubly-fed induction generator (DFIG). In this paper, a new compensation algorithm of the current measurement errors is proposed. The main concept of the proposed algorithm is implemented by integrating 3-phase rotor currents in the stationary reference frame to compensate the measured current ripples in DFIG (Doubly Fed Induction Generator). This algorithm has several features: easy implementation, less computation time, and robustness with regard to the variation of the electrical parameters. The experiment results show the effectiveness of the proposed algorithm.

This paper presents a comparative study of conventional spectrum estimation methods, such as the periodogram method, and modern techniques, such as the autoregressive and multiple signal classification methods, for current mapping by a high frequency surface wave radar. To calculate the radial current velocity, it is important to estimate its associated Doppler shift from the frequency spectrum. In addition to the conventional centroid method, a more robust Bragg frequency identification method, termed the symmetric-peak-sum, is proposed and examined in conjunction with each of the spectral estimation techniques. It has been found that a weighted sum of the radar-derived current estimates using these two methods generally provides a lower rms difference from the buoy measurements. The weighting ratio is optimized using a genetic algorithm. Field data indicate that a combination of these spectral estimation methods is capable of providing improvements in retrieved current velocities for various current conditions.

Compared with onshore bistatic high frequency surface wave radar (BHFSWR) and shipborne monostatic HFSWR (MHFSWR), shipborne BHFSWR combines the common advantages of both. It is worth noting that the motion state of both mobile stations needs to be considered. In this paper, the spreading characteristics of first-order sea clutter and the doppler shift of the shipborne BHFSWR were analysed theoretically. Then, the simulation results of forward motion and yaw motion are given, and the co-modulation of the echo signal by the motion of the transmitting and the receiving station was investigated. The simulation results show that the amplitude-frequency characteristics of the first-order sea clutter were more complex when the shipborne transmitter and shipborne receiver were at different uniform motion. Finally, combined with the flexibility of shipborne platform, a new scheme was proposed. That is, it can be adjusted the posture information of the shipborne platform, the detection coverage can be better and the spread range of the first-order sea clutter can be reduced.

Due to the unbalanced three-phase currents and the torque ripples and speed fluctuations to the drive caused by the current sampling errors of three-phase current sensors and corresponding signal processing circuits, the estimation on current sampling errors is of great importance to eliminate these adverse effects. The observer-based methods, however, present two defects: first, they could increase the computational burden to the drives, thus limiting their practical applications; second, their high dependence on the system speed or position feedback information might degrade the estimation accuracy. To solve these problems, this article proposed a fixed-point sampling method of lower branch flows and three phase-currents, which adopts the relations between the measured currents, rather than any complex algorithm, to estimate the current sampling errors. To coordinate with the proposed strategy, a fixed-point sampling scheme was accordingly rolled out. Compared with the observer-based methods, as a fixed-point one, much applicable to actual digital controllers, it could reduce the computational burden of the drive. Meanwhile, its effectiveness was verified by experimental results on a 1.5-kW IPMSM prototype, which showed accurate estimated current sampling errors and the effectively eliminated torque ripples and speed fluctuations.

AC servo motors are operated by current sensors. However, these current sensors often exhibit offsets caused by age-related deterioration and thermal variation. Gain deviation also causes current measurement errors. These errors lead to torque ripples and dq-axis current ripples. This paper proposes a new method of estimating and compensating for gain deviation in current measurement using a current simulator. The method uses dq-axis current ripples arising from sensed current and estimated current. Thus, it can be applied in conjunction with a proposed offset estimation method. In this paper, the methods are tested on a surface-mounted permanent magnet synchronous motor, in which the current sensors are arranged in u-phase and w-phase. Simulation and experimental results confirm that the proposed methods suppress current and torque ripples caused by offset and gain deviation in current sensors.