Hu Weidong

A unified out-of-sequence measurements filter

When sensor data from multiple platforms is collected and fused in centralized manner, sensor measurements can arrive out-of-sequence at the central processor due to varying pre-processing times at the platforms and uncertain data transmission delays in communication networks. As a result, classical filters for orderly measurements, such as Kalman filter, cannot be used directly. There are three classes of OOSM (out-of-sequence measurements), respectively called single-lag OOSM, multiple-lag OOSM, and mixed-lag OOSM. For the first two classes of OOSM, C.A. Stelios et al (1988), R.D. Hilton et al., (1993), Y. Bar-Shalom (2002), and M. Mallick et al. (2001) have studied some optimal or suboptimal filters. This paper develops a recursive filter algorithm called UOOSMF (unified out-of-sequence measurements filter), which can sequentially process the OOSM in order of the time of arrival and is suboptimal in the linear MMSE sense under one acceptable approximation. The filter is accommodated with all the above three classes of OOSM. With the same measurements, the estimation accuracy of the UOOSMF is almost the same as standard Kalman filter for orderly measurements, and its computations and memory requirements are low. Numerical example proves the above conclusions.

Local Degrees of Freedom of Airborne Array Radar Clutter for STAP

In this letter, the local degree-of-freedom (LDOF) theorem for reduced-dimension space-time adaptive processing (STAP) methods is presented, and a rigorous proof is provided. LDOF is more valuable for practical STAP methods than conventional full degrees of freedom. The effectiveness of the LDOF theorem is verified, and the influences of some operations in practice on the LDOF are analyzed by simulations.

A new method of HRR profile formation based on multiple radars LFM signal fusion

In this paper, the authors present a new method of HRR profile formation based on the LFM signal fusion of the multiple radars with multiple frequency bands. The principle of the multiple radars signal fusion improving the range resolution is analyzed. During the fusion process, the B-splines interpolation and an entropy-minimization principle based phase compensation algorithm are applied. The theoretical analysis and simulations results show the proposed method can effectively increase signal bandwidth and provide a high resolution range profile

Precession Period Extraction of Ballistic Missile Based on Radar Measurement

A method of extracting the precession period of ballistic missiles (BM) in the midcourse phase of their ballistic trajectories with radar cross section (RCS) sequence was presented. The motion of ballistic missiles in the midcourse phase was analyzed and modeled. Then the relationship between the precession period of missile warhead and its RCS sequence was discussed. Based on the variation characteristics flying in the RCS sequence, a method of extracting the precession period was proposed. Simulation results demonstrated the efficiency of the method

Fractal detection of radar weak targets

Radar weak target detection is a very difficult problem. The fractal information of radar targets may provide a new way to enhance the performance of conventional detection methods. A new approach to detect ship targets from sea clutter has been developed. The approach is based on the use of the fractal signature difference between the ship target and sea clutter, which is manifested in the backscattered radar signal from different objects. The fractal signatures are estimated by using the box counting method. In the case of a small number of sampling points, the computation of the fractal dimensions is considered.

Impulsive noise suppression using morphological operators

A new approach to impulsive noise suppression found in desired signals is presented using mathematical morphological operators that incorporate the shape information of the signal in this paper. A brief introduction to these nonlinear signal processing operators, as well as a detailed description of the new algorithm, is presented. Experimental results show that the new algorithm has good performance in impulsive noise suppression.

Novel cued search strategy based on information gain for phased array radar

A search strategy based on the maximal information gain principle is presented for the cued search of phased array radars. First, the method for the determination of the cued search region, arrangement of beam positions, and the calculation of the prior probability distribution of each beam position is discussed. And then, two search algorithms based on information gain are proposed using Shannon entropy and Kullback-Leibler entropy, respectively. With the proposed strategy, the information gain of each beam position is predicted before the radar detection, and the observation is made in the beam position with the maximal information gain. Compared with the conventional method of sequential search and confirm search, simulation results show that the proposed search strategy can distinctly improve the search performance and save radar time resources with the same given detection probability.

Adaptive Beam Scheduling Algorithm for an Agile Beam Radar in Multi-Target Tracking

An adaptive beam scheduling algorithm based on covariance control strategy is proposed for an agile beam radar in multi-target tracking applications. The algorithm uses IMMKFs (interacting multiple model Kalman filters) to track maneuvering targets. The optimal beam scheduling model is built which can schedule radar beam based on the difference between the desired covariance matrix and that of the actual covariance of each target for maintaining the targets state estimate covariance near a desired level. Two kinds of beam scheduling algorithms, the minimum mean bias criterion and the minimax bias criterion, are proposed, respectively. Simulation results show that the algorithms based on covariance control can quickly achieve the desired tracking state and allocate sensor resources effectively

The Imaging Principle and Method based on Distributed Multi-channel radars

We propose the imaging principle based on distributed multi-channel radars, in which multiple sparse transmitters and receivers are appropriately arranged according to the request about wave-number samples. The imaging radar can reconstruct target two-dimensional reflectivity function by coherent processing the echo signals in multi-channels. Moreover, large antennas are not required, and movement compensation need not be performed. For the distributed radars with the given distribution of transmitters and receivers, the phase errors are analyzed and the coherent processing methods in ISAR technique are applied to image targets. Finally the simulations are given to validate the imaging performance

Multi-cycle Integration of Weak Target Signals in LFMCW Radar

This paper presents a new way for integration and detection of weak signals with large Doppler frequency in linear frequency-modulated continuous wave (LFMCW) radar systems. With secondary mixing of up-sweep beat signal and down-sweep beat signal, the range-Doppler coupling can be resolved effectively, and a new signal with only Doppler frequency information is brought out. By two-dimensional FFT (fast Fourier transform), multi-cycle weak signals can be integrated to get high signal-to-noise ratio (SNR) for target detection and parameters estimation. Using peak detection, target velocity is estimated first. Motion compensation then follows according to the velocity estimated, and target range can be found out also through two-dimensional FFT. And performance analysis is carried out finally. The efficiency of the method is proved by the simulation results