Implementation of method for operating multiple high frequency surface wave radars on a common carrier frequency

Recently, the usage of high frequency surface wave radars has been expanded to monitoring of ships within observable region because it can always observe wide target region with low power consumption. However, ship monitoring systems using high frequency radars suffer from the fact that a detection position is far away from true position. To resolve this problem, the proposed method determines final location by applying Bayesian estimator to detection results from each ship monitoring system in high frequency radar network. According to Bayesian theory, a posterior distribution is factorized into likelihood and prior distributions, and both distributions are modeled by using each detection results and auto-identification system data, respectively. Effectiveness of the proposed method is demonstrated through appropriate synthetic and real data. From the results, location accuracy can be improved when the proposed method is applied to location estimation.

Doppler frequencies generated by the navigating ship targets may coincide with the first order Doppler spectrum of the sea clutter in high frequency surface wave radar (HFSWR). Therefore, the ship targets fall in this clutter spectrum were usually undetectable by the traditional processing method in HFSWR. In this paper, we introduced a spatial blind filtering method based on oblique projection, which utilized the characteristic difference of ship target and sea clutter returns in spatial directions. Different from the broad beam pattern formed by the conventional digital beamforming technique, the method proposed realized clutter suppressing within the broad beam when the notches are sufficiently dense by blind filtering. The ship target in sea clutter was detected in filed data processing results of HFSWR and it was further verified by the synchronous automatic identification system (AIS) information collected. Those demonstrated that our method can detect the ship targets in sea clutter and this spatial blind filtering technique had a great potential in solving this problem in HFSWR.

In many applications, the interacting multiple model (IMM) estimator provides better performance over the single model Kalman filter (KF). However, there are two high frequency surface wave radar (HFSWR) factors which seem to make the IMM estimator less attractive: Firstly, the radar has a very long integration time, up to a few hundred seconds for ship detection; Secondly, the radar has very limited detection capability of maneuvering targets. It is unknown whether the IMM estimator will perform better over an EKF for real time HFSWR. The purpose of this paper is to compare the EKF with the IMM estimator. An optimal noise level and critical value have been found for HFSWR EKF design. The results demonstrated that the IMM estimator does not yield improved performance.

A bistatic high-frequency surface wave radar (HFSWR) with both receiving and transmitting stations placed on different ships (platforms) is a new radar system and referred to as shipborne bistatic HFSWR. In this paper, a first-order ocean surface cross section of shipborne bistatic HFSWR was derived. The first-order cross-section models for three different cases, i.e., ships moving with uniform, periodic, and hybrid motion states, respectively, are presented. The corresponding first-order Doppler spectra were simulated, and the spread width of the first-order spectrum was investigated. The simulation results show that the characteristics of the first-order spectrum are similar to those of a shore-based bistatic HFSWR when the transmitting and receiving platforms move in opposite directions. The first-order spectral spread width in the case of platforms with opposite directions is much smaller than that in the case of platforms with the same direction. This finding is useful for reducing HFSWR first-order spectrum spread due to platform motion, thus improving the target detection performance of the shipborne bistatic HFSWR. In addition, periodic oscillation motion of both platforms will cause complex motion-induced peaks in the first-order spectrum, which may be detrimental to target detection and ocean remote sensing. These results have important implications for the application of shipborne bistatic HFSWR.

Land or sea clutter Doppler frequency shift and spectrum spread are traditionally attributed to the radar platform motion relative to the clutter scatterer. However, another spreading mechanism of the clutter Doppler spectrum exists in onshore bistatic high frequency surface wave radar (HFSWR), which is presented in this study. Based on the space-time distribution of the first-order sea clutter in onshore bistatic HFSWR, the spreading model is proposed, which is verified by the simulation and experimental results. It demonstrates that the space-time model would be of fundamental importance to the future experimental investigations, not only of target detection but also of ocean remote sensing with onshore bistatic HFSWR.

Due to the incapability of high frequency surface wave radar (HFSWR) to detect undercurrent parameters, a new algorithm is proposed to apply a three-dimensional (3D) nonlinear barotropic shallow sea and continental shelf model in coordinate system to the inversion of undercurrent. The calculation domain of this model is the area detected by HFSWR. Considering the benthal topography of the detected area and the ocean dynamic parameters, such as surface current, wind and wave detected by HFSWR, the relation between surface current and undercurrent is established in this model. Accordingly, the undercurrent parameters of corresponding area are obtained. The inversion results agree with the law of ocean dynamics and reproduce the movement of undercurrent.

Because of the complexity of the marine environment, the High Frequency Surface Wave Radar (HFSWR) in dealing with marine information will be accompanied by a lot of interference and noise. To solve this problem, first of all, we use the logic method to measure the trace information of the radar in order to find out the logical starting track. Secondly, the Kalman filter is used to predict and calculate the trajectory based on the existing starting track. So as to realize the purpose of finding the track in the complex HFSWR track signal. In this paper, The data comes from HFSWR data and Automatic Identification System (AIS) data on China's Bohai Bay. Finally, in order to show the accuracy of the method, the HFSWR track results calculated by the method used in this paper are compared with the track results of AIS.

A bistatic/monostatic HF surface wave radar (HFSWR) experiment was conducted using both the transmit and receive systems of the HFSWR at Cape Race, Newfoundland and the receive system of the HFSWR at Cape Bonavista, Newfoundland. While the HFSWR at Cape Race operated in the monostatic mode, the transmitter at Cape Race and the receiver at Cape Bonavista were synchronized via the Global Positioning System to operate in the bistatic mode. The results of the experiment show that the range of the clutter return from a specified layer in the ionosphere in the bistatic radar, and the range of the clutter return from the same layer in the monostatic radar follow, approximately, the relationship specified by the Pythagorean theorem, i.e., R=(h/sup 2/+(L/2)/sup 2/)/sup 1/2/, where h is the virtual height of the specified layer in the ionosphere, which is also the range of the clutter return in the monostatic radar case, and L is the separation between the transmitter and receiver in the bistatic configuration. Based on these observations, we calculated the coverage areas of bistatic HFSWR systems for water-entry areas along coastlines. The calculation shows that a bistatic HFSWR system has the potential to significantly improve the nighttime coverage area over a monostatic system, while, approximately, maintaining the same daytime coverage.

Assessment of actual detection capabilities obtained with high frequency surface wave radar (HFSWR) is a key issue for the global surveillance of the exclusive economic zone (EEZ). ONERA (The French Aerospace Lab) has just finished a ten month trial cycle of its new full digital HFSWR system. This system uses digital signal generation, digital receivers, high computational power workstations and low power transmitters. All results extracted from the trial database are compliant with the theory in particular concerning sea clutter and ionospheric clutter. Moreover, the detection of vessels up to 170 km and aircrafts up to 75 km (results obtained with cooperative targets and ground truth data) shows the over the horizon capabilities of the system. Finally, clutter reduction prospects are also presented.

Direct target localization and vector-velocity measurement method based on bandwidth synthesis in distributed high frequency surface wave radar

This paper proposes a new method to estimate the target altitude of an aircraft using high frequency surface wave radar(HFSWR) for the surveillance of the Exclusive Economic Zone. The initial position and velocity in 3-D coordinates are estimated based on quasi-Newton method from a sequence of measurements, this initial values are incorporated for 3-D tracking with the modified iterated extended Kalman filter(IEKF), a multiple model approach is adopted to improve the tracking accuracy. The simulation results demonstrate the effectiveness of the proposed method.

The detection performance of high frequency surface wave radar is impacted by the ionospheric clutter. In order to suppress the ionospheric clutter, it is important to research the physical mechanism of ionosphere. In this paper, it is demonstrated, convincingly for the first time, the chaotic dynamics of the ionospheric clutter. The results are obtained using the actual data collected from a series of experiments in Weihai Radar Station, China. The study includes the stationarity and nonlinearity test, phase-space reconstruction, correlation dimension analysis and Lyapunov exponents analysis. And finally, it is found that ionospheric clutter in HFSWR is a chaotic dynamical process with relatively few degrees of freedom, in contrast to the stochastic approach where the ionospheric clutter is viewed as a random process with a large number of degrees of freedom[1]. Also, this new approach will further provide an original way to analyse the ionospheric clutter suppression method.

High frequency (HF) interference in surface wave over-the-horizon (OTH) radars typically exhibits a time-varying or non-stationary spatial structure. Adaptive beamformers that update the spatial filtering weight vector within the coherent processing interval (CPI) are likely to suppress such interference most effectively, but the intra-CPI antenna pattern fluctuations result in temporal de-correlation of the clutter which severely degrades sub-clutter visibility after Doppler processing. A robust adaptive beamformer that effectively suppresses non-stationary interference without degrading sub-clutter visibility is proposed. The new algorithm is computationally efficient and suitable for practical implementation. Its operational performance is evaluated using experimental data recorded by the Iluka HF surface wave (HFSW) OTH radar, located near Darwin in far north Australia.

This paper demonstrates the benefits that high-frequency surface wave radars (HFSWR) are bringing to maritime safety and security in off-shore activities at over the horizon distances. As a primary means for remote sensing of marine and maritime environment, a network of HFSWRs is deployed in the western part of the Gulf of Guinea and covers an area of over 100 km2. Alongside HFSWRs, usual maritime sensors are utilized for vessel tracking as well, however, only satellite automatic identification systems (SAIS) and land automatic identification systems (LAIS) are capable of covering over the horizon distances. Unfortunately, both LAIS and SAIS require vessel cooperation in order to provide any data, which is often abused by vessels conducting illegal activities. Here, analysis is done in which AIS and HFSWR data are compared in order to identify a pattern of behavior of non–cooperative vessels (vessels with onboard AIS devices turned off) so a proper risk assessment may be achieved. It is shown that typical patterns can be easily recognized for two illegal activities which plague the waters where this study is conducted. Those illegal activities are oil bunkering and piracy, both conducted off-shore and out of the reach of the usual coastal sensors such as X or S band radars. Furthermore, tracks created whilst conducting illegal activities are easily distinguishable from others in the overall operational picture. Additionally, it should be pointed out that numerous vessels are switching off their AIS devices when they leave the coastal regions in order to avoid detection by pirate vessels. This behavior can also be easily recognized and must not be mixed with the illegal activities mentioned above.

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.