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Besides the noise and possible target signal, the data received by radar often contains interference, which can significantly degrade the detection performance. This paper considers the problem of detecting a target in partially-known interference and unknown Gaussian noise. The interference is unknown, but it is constrained in a subspace orthogonal to the signal in the whitened space. According to the generalized likelihood ratio test (GLRT) criterion, we proposed an effective detector, which has improved detection performance than the corresponding Rao and Wald tests. Remarkably, the GLRT in the presence of interference has the same form as that in the absence of interference. However, the interference affects the detection performance of the detector. The effectiveness of the proposed detector is demonstrated by Monte Carlo simulations.

When detecting low-attitude targets, the angular accuracy of the meter-wave radar will descend because of the multipath reflecting from ground. Since the multipath signal can be regarded as a multipath jamming coming from the mainlobe, we propose an angle measurement based on the mainlobe multipath jamming suppression. The weight of the new approach is obtained through certain constraints on the sum-difference beams, which can suppress the jamming and keep the curve of the monopulse ratio unchanged. Then form sum-difference beams and calculate the ratio of the monopulse ratio of the target. Finally, obtain the angle of the target through the determination of the smallest difference between the monopulse ratio of the target and the quiescent monopulse ratio. The method proposed in this paper can preserve good precision of the measurement angle. The result is verified by computer simulation.

This paper addresses the issues of clutter characteristics of airborne MIMO radar in presence of degradation of transmit waveforms orthogonality. Firstly, synthesis subarray space and synthesis transmit signal space caused by the degradation are illustrated. Clutter model of airborne MIMO radar is developed under the orthogonal degradation. Clutter models with arbitrary waveform are unified in this model via the transform of array space and signal space. Secondly, an estimation rule for the number of clutter DOFs is presented based on band-limited signal theory. It shows that clutter DOFs is dependent on both the number of inner elements in synthetic subarray and synthetic signal. Finally, clutter characteristics under the influence of degradation are analyzed compared with the ideal MIMO radar model and phase array radar, and the effectivity of the estimation rule is verified in the numerical examination.

To solve the problem of nonhomogeneous clutter suppression for moved target detection in the airborne phased array, this paper presents a new space time adaptive processing (STAP) method which is named D3AR. This method firstly filters the target information of cell under test (CUT) based on the phase information of target. Then constructs the AR model to obtain the clutter characteristic. Finally gets the space orthogonal to the clutter subspace based on the AR filter coefficients. This method doesn't require the adjacent training data and is suited for nonhomogeneous clutter environment. The simulation results demonstrate its effectiveness.

For along-track multi-channel synthetic aperture radar (SAR), this paper proposes a novel ground moving target signal model in the high-resolution complex image domain. It is shown that moving targets can be divided into three types according to the 2D motion distribution and the SPP approximation conditions. Moreover, a single target can be split into two targets in the image. All types of targets will have the same Doppler interferometric effect along multichannel images, which is decided by the target's ambiguous Doppler frequency. Furthermore, with our derived signal model, the complex image properties, i.e., amplitude reduction, azimuth shift, azimuth defocus, range blur, 2D slant, second-order phase modulation, split, interferometry and the effects of 2D accelerations are analyzed for airborne and spaceborne SAR, respectively. Finally, some experimental results are also provided to demonstrate the effectiveness of the proposed signal model and analysis.

For bi-static synthetic aperture radar (Bi-SAR) with phased array antenna and arbitrary bi-static geometry configuration, this paper proposes a clutter modeling method for clutter suppressing and ground moving target detection. Furthermore, based on the simulated space-time clutter spectrum of four typical specific geometry configurations, the detailed analysis is also given for the clutter suppressing performance with space-time adaptive processing (STAP), Finally, some important conclusions are given for Bi-SAR with different geometry configuration.

With the rapid development of computer simulation technology, the Radar simulation applications scale up increasingly. More and more Radar simulation applications adopt distributed structure to improve system performance and availability. Hence, how to enhance the robustness and efficiency of these complex distributed simulation systems is a hot point. We should balance the load for the applications to enhance the resource’ s utility and increase the throughput. To overcome the problem, one effective way is to make use of load balancing. At the same time, load balancing middleware provides better scalability, response time and throughput. However, we must pay attention to the fact that the computing of the load should be adaptive and predicative to avoid the affection of the peak load. To the complex simulation applications, the peak means the system may suffer extremely high load for a short period while keeping stable load for a long time and some hosts of the system may be overloaded and the response time may be decreased for this kind of fluctuate. Therefore, to utilize the services effectively especially when the workloads fluctuate frequently, we should make the system react to the load fluctuate gradually and predictably. So we have proposed and implemented machine learning based load prediction and fuzzy logic based replica management for adaptive and flexible load balancing mechanism within the framework of distributed middleware.

Multipath reflection has great influence on the meter-wave radar low-elevation height-finding. In this paper, a computationally efficient method is presented fully using the information included in the received data covariance matrix. This method is proposed according to the spatial smoothing concept and the propagator operator method. The presented method constructs new forward and backward matrices based on the spatial smoothing idea, and then summing the obtained forward and backward matrices obtains the noise subspace with the propagator operator method. This algorithm estimates DOAs without the need to decorrelate and eigen-decompose. Therefore, the method has good estimation performance for the meter-wave radar height-finding with a small number of snapshots and at low SNR. Simulation experiments and real data processing results both demonstrate the effectiveness of the proposed method in the meter-wave radar low-elevation height-finding applications.

Based on the parametric velocity synthetic aperture radar (VSAR), the novel model of VSAR with azimuth multi-look processing is first established in this paper. Furthermore, Cramer-Rao bounds (CRBs) are derived for the VSAR model parameters estimation based on the maximum likelihood (ML) methods. It is shown that the clutter parameter estimation accuracy may be improved via azimuth multi-look processing as long as clutter-to-noise ratio is not too low. Finally, some numerical experiments are also provided to validate the proposed model and methods.

In this paper, a novel method based on Toeplitz decorrelation is proposed to estimate DOAs (direction of arrivals) of the coherent sources. Using the eigenvector corresponding to the largest eigenvalue obtained from the received data covariance matrix, the presented method constructs a new Toeplitz matrix whose rank is equal to the number of sources. The presented method fully uses the property of the eigenvector corresponding to the largest eigenvalue to estimate coherent sources. This approach doesn't lose the array aperture and can estimate more coherent sources without spatial smoothing and weighting. In comparison with the conventional decorrelation algorithms, the method offers better estimation performance for coherent sources, especially at low SNR. Theoretical analyses and simulation experiments demonstrate the effectiveness of the proposed method for estimation DOAs of the coherent sources.

By dividing a vector sampling at a specified pixel into three components, namely target, clutter, and noise, a novel parametric statistical model is proposed in the multi-channel and multi-look image domain of velocity synthetic aperture radar (VSAR). With the proposed parametric model, an adaptive moving target detection method is also obtained for VSAR, based on which the optimum improvement factors may be achieved. Furthermore, discussions are given for some VSAR systemic parameters. Some detailed numerical experiments are provided to demonstrate the effectiveness of the proposed model and the parametric methods of target detection.

Space-Time Adaptive Processing (STAP) is the key technology of new generation airborne digital signal processing system. The primary differences between STAP methods are the order of the operations performed and the choice of transformation matrix. The operations performed in different methods are similar to each other, such as FFT and QR decomposition. This similarity makes the idea of universal platform be very attractive. Based on a parallel computational model the model, a universal real-time platform for STAP is developed. Scalable cluster-organized multi-processors structure is adopted as hardware architecture. Common operations are built into software libraries. And a heuristic methodology is used for task mapping. Two STAP methods are applied. Results show that the universal platform can meet the real-time requirement, and its scalability is good.

Space-time adaptive processing (STAP) is the key technology of new generation airborne digital signal processing systems. A major challenge in implementing STAP is to meet the real-time requirement while using fewer processors. To meet these requirements, a unified framework for STAP task mapping is developed. In this framework, a STAP task model is firstly brought forward, which exploits the fact that tasks in each stage are independent. Different types of processors are considered. And a four-step task mapping methodology is developed. Experimental results show that our methodology can save processors while meeting real-time requirement.

The non-sidelooking array is different from the sidelooking array in that its Doppler frequency of the clutter spectrum varies with the range and its clutter degree of freedom (DOF) is increased, so that the conventional STAP methods are not feasible. The clutter distribution of airborne non-sidelooking array radar is analyzed and investigated in detail, and a new clutter suppression approach to airborne phased radar with non-sidelooking array based on covariance matrix taper (CMT) is proposed. The simulation results verify the effectivity of the method

Suboptimum performance may be obtained by partially adaptive processing at the subarray stage where the problem of how to group the subarrays is an important task. In this paper, an effective subarray partition method based on genetic algorithm is proposed, which divides a uniform linear array employing amplitude tapering into contiguous subarrays of unequally sized. In the genetic algorithm, the crossovers are prevented by including an appropriate penalty term in the cost function. The simulation results are given to show that the subarray configuration has few obvious grating lobes while its clutter suppression performance is close to that at the element stage