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In addition to target echoes, high frequency surface wave radar (HFSWR) receives sea and ionospheric clutter. Among these clutters, the ionospheric clutter is dominant and significantly affects the detection performance of HFSWR, particularly when the targets are located 100 kilometers away from the radar, rendering it an unsolved problem for HFSWR. Existing studies concerning HFSWR ionospheric clutter lack empirical research on the nonlinear dynamical characteristics of the ionosphere of HFSWR as existing ionospheric suppression methods are still insufficient to adapt the project application. Therefore, the present study utilized the threshold segmentation method to eliminate the sea clutter in HFSWR Range-Doppler spectrum and extracted ionospheric signals from this spectrum by edge feature extraction. Subsequently, the chaotic invariants, such as correlation dimension and the largest Lyapunov exponent of HFSWR ionospheric clutter, were calculated by phase space reconstruction, whilst the chaotic dynamical characteristics of HFSWR ionospheric clutter were determined by the 0-1 test for chaos and other algorithms. Furthermore, the present study demonstrated, for the first time, the chaotic dynamics of the ionospheric clutter of HFSWR with a low-dimensional attractor by processing and analyzing the experimental data from the Weihai High Frequency Radar Station. The conclusion redefines HFSWR ionospheric clutter based on chaotic dynamics rather than regarding it as a stochastic process, which is conducive to efforts to explore the formation mechanism of ionospheric clutter in essence, which can ultimately improve the detection capability of HFSWR, particularly for long-distance targets.

High frequency surface wave radar (HFSWR) is traditionally unable to detect target altitude information. To simultaneously estimate the target altitude and radar cross-sections (RCS) with bistatic HFSWR, a novel estimation model is proposed with the variation of the propagation attenuation, and the target echoes are utilized to construct the measurement equation. The presented model is completely observable to the target state, which can contribute to preferable estimation results. In order to improve the estimation accuracy, a centralized fusion model is adopted to fuse four measurement vectors. Simulations and practical examples demonstrate the effectiveness of the proposed estimation model.

When particle filter is applied in radar target tracking, the accuracy of the initial particles greatly effects the results of filtering. For acquiring more accurate initial particles, a new method called “competition strategy algorithm” is presented. In this method, initial measurements give birth to several particle groups around them, regularly. Each of the groups is tested several times, separately, in the beginning periods, and the group that has the most number of efficient particles is selected as the initial particles. For this method, sample initial particles selected are on the basis of several measurements instead of only one first measurement, which surely improves the accuracy of initial particles. The method sacrifices initialization time and computation cost for accuracy of initial particles. Results of simulation show that it greatly improves the accuracy of initial particles, which makes the effect of filtering much better.

In practical multi-sensor information fusion systems, there exists uncertainty about the network structure, active state of sensors, and information itself (including fuzziness, randomness, incompleteness as well as roughness, etc). Hence it requires investigating the problem of uncertain information fusion. Robust learning algorithm which adapts to complex environment and the fuzzy inference algorithm which disposes fuzzy information are explored to solve the problem. Based on the fusion technology of neural networks and fuzzy inference algorithm, a multi-sensor uncertain information fusion system is modeled. Also RANFIS learning algorithm and fusing weight synthesized inference algorithm are developed from the ANFIS algorithm according to the concept of robust neural networks. This fusion system mainly consists of RANFIS confidence estimator, fusing weight synthesized inference knowledge base and weighted fusion section. The simulation result demonstrates that the proposed fusion model and algorithm have the capability of uncertain information fusion, thus is obviously advantageous compared with the conventional Kalman weighted fusion algorithm.

The scheme for tracking maneuvering target based on neural fuzzy network with incremental neural learning is proposed. When tracked target maneuver occurs, the scheme can detect maneuver immediately and estimate the maneuver value accurately, then the tracking filter can be compensated correctly and duly by the estimated maneuver value. When environment changes, neural fuzzy network with incremental neural learning (INL-SONFIN) can find its optimal structure and parameters automatically to adopt to changed environment. So, it always produce estimated output very close to the true maneuver value that leads to good tracking performance and avoids miss-tracking. Simulation results show that the performance is superior to the traditional schemes and the scheme can fit changed dynamic environment to track maneuvering target accurately and duly.

This paper describes the principle of the target detection and estimation with a high frequency ground wave over-the-horizon radar, introduces the structure and the implementation of the detection and estimation subsystem that can process signals in parallel. The accomplished experimental results demonstrate that this system can successfully detect and estimate the over-the-horizon ship and aircraft.

In this paper, a multi-channel digital Stretching structure applied to image multi-ships simultaneously is proposed and which is very important for the ISAR to be used in ocean environment with intensive ships. A base band width expression of ship echoes which stands for the maximum length in radial range dimension occupied by the target being imaged is deduced. Based on the fact that ISAR emission signal band width is much wider than the base band width of the target echoes, a fast multi-phase filtering algorithm which leads to a sharply reduction of the computation load in range information processing is introduced and a matrix method of the algorithm is presented. Finally, a formula for estimating the computation load in terms of the radio of radar signal bandwidth to target's base band width is given, and the correctness of the equation is verified by an example from an actual ISAR system.

Azimuthal measurements of the high frequency ground wave over-the-horizon (OTH) radar are either coarse or unavailable in an actual environment. It is very hard for a single station OTH radar to performance effective tracking under that condition. A bi-station OTH radar system is proposed to triangulate target tracks using range and Doppler measurements only. Multi-sensor data association and combining positioning are key issues of this system. This paper focuses on positioning and tracking filter algorithm. The locating and tracking model is established and the extended Kalman filter (EKF) is used to solve the bi-station nonlinear tracking problem. Simulation result shows that EKF can handle this well, and the bi-station OTH radar system can meet or slightly better than the accuracy of the conventional single station OTH radar tracking system with poor bearing resolution.

In a complex environment radar plot is often changing dramatically, non-uniform, discontinuous, uncertain and containing lots of false alarms. It is difficult for tracking system to form tracks in such complex environment. Traditional track formation methods can not accurately determine the size of track initiation gate and select specific initiation criteria. In this paper, an intelligent information processing method is proposed to solve track initiation in complex environments. The track initiation is considered as a progress of plot identification, classification and fuzzy information processing. According to the integration and fusion of neural network (NN), fuzzy reasoning (FR) and expert system (ES) technology, an intellectualized track initiation knowledge reasoning system is constructed which can realize structural, functional, algorithmic and hierarchical complementation. Further more the adaptive robust learning algorithm and the weighted synthesis reasoning algorithm are used in this system. Real data experiment shows that the proposed knowledge engineering method can effectively solve track initiation problem in complex conditions.

Though a Monostatic-Bistatic Composite High Frequency Surface Wave Radar Network (MBC-HFSWR Network) is established to cover the shortage of the monostatic radar, how to judge the real target update from the plausible measurements to initiate tracks quickly and accurately still remains a challenging problem. A novel method utilizing unconventional characteristics for track initiation is proposed in this paper, which uses more accurate information and avoids the problem of low detection precision of high frequency radar. A highly intelligent information fusion model which is a combination of Neural network, Fuzzy reasoning and Expert system (NFE model) is presented to solve the problem above and the validity of the provided method is demonstrated by Monte Carlo simulations.

High frequency surface wave radar (HFSWR) is superior to detect and track beyond-the-horizon targets but susceptible to the ionosphere interference because of the specific wavelength. Seeking out the real target points from plenty of false ones is an obsessional problem for data association. This is commendably solved by establishing a new radar system, the monostatic-bistatic composite radar network, and providing a novel process data association with steps of space cursory association, time cursory association, etc. The validity of this method is substantiated by the simulation results.

In particle filter algorithm, resampling is always used to release sample impoverishment phenomenon, but it weakens the diversity of samples set and cause the algorithm unrobust. Based on imitating biology evolvement regulation, paper (Mo Yi-wei, et al., 2005) brought forward the evolutionary particle filter (EPF) algorithm. On the cost of much calculation, this method ameliorates the diversity of samples set to relieve the effect caused by samples impoverishment, but in paper (Mo Yi-wei, et al., 2005) the way to select the variation strength is not related. In radar tracking, this paper brings forward an improved evolutionary particle filter (IEPF) algorithm, in which variation strength is based on state noise and measurement noise. What's more, unlike EPF in previous paper, in which the evolution proceeds at each step, the algorithm sets threshold of effective particles to determine if variation is necessary at current step, and much calculation is saved. Simulations demonstrate the feasibility of proposed algorithm

In the background of signal detection for HF radar, the sea clutter is quite significant and can mask some weak target signals. This paper reports a chaotic local prediction based sea clutter cancellation method to improve the SNR of the target. Recent observation shows that HF sea clutter may be modeled as a nonlinear deterministic dynamical system. Then it can be assumed that the chaotic prediction error of target echo is greater than the prediction error of sea clutter. A local chaotic predictor is used to construct a sea clutter cancellation technique. Real sea clutter, simulated target data and real target data were used to show that a chaotic prediction based clutter cancellation method is a promising technique to suppress sea clutter and enhance target detection.

This paper presents an adaptive neural networks-fuzzy reasoning information fusion system which is employed to decrease the influence of the uncertainty of sensor state on fusion performance under a complex environment. The model consists of a confidence estimator, fusing weight knowledge base and weighted fusion. When utilizing the new model, we can obtain the confidence of each sensor and the reliable fusion data with an adaptive synthetical computation of sensor state, sensor detection precision and track filtering error based on the techniques of neural networks, fuzzy reasoning and knowledge base. The simulation results show that the new fusion model is obviously advantageous compared with the conventional Kalman weighted fusion.

A maneuvering target tracking algorithm for a multisensor system using an image sensor and other different sensors is presented; it combines a variable dimension filter (VDF) with an interacting multiple model (IMM). This technique uses an IMM estimator instead of the single model filter used in VDF when the maneuver detector declares an onset of maneuver. The performances of this approach, and of the VDF and IMM algorithms are investigated and compared. During a nonmaneuvering period, this approach is better than the IMM algorithm and equal to the VDF algorithm. During a maneuvering period, especially right after the end of a maneuver, this approach provides better estimations than the VDF algorithm.

Methods based on wavelet transform theory for decreasing sampling noise in feedforward neural networks are proposed in this paper. Wavelet bases are employed in the network to constrain the network's ability in learning samples which are corrupted by noise. The selection of the wavelet bases which correlate with the map to be approximated is mainly discussed.