High Speed Dim Air Target Detection Using Airborne Radar under Clutter and Jamming Effects

The challenging potential problems associated with using airborne radar in detection of high Speed Maneuvering Dim Target (HSMDT) are the highly noise, jamming and clutter effects. The problem is not only how to remove clutter and jamming as well as the range migration and Doppler ambiguity estimation problems due to high relative speed between the targets and airborne radar. Some of the recently published works ignored the range migration problems, while the others ignored the Doppler ambiguity estimation. In this paper a new hybrid technique using Optimum Space Time Adaptive Processing (OSTAP), Second Order Keystone Transform (SOKT), and the Improved Fractional Radon Transform (IFrRT) was proposed. The OSTAP was applied as anti-jamming and clutter rejection method, the SOKT corrects the range curvature and part of the range walk, then the IFrRT estimates the target’ radial acceleration and corrects the residual range walk. The simulation demonstrates the validity and effectiveness of the proposed technique, and its advantages over the previous researches by comparing its probability of detection with the traditional methods. The new approach increases the probability of detection, and also overcomes the limitation of Doppler frequency ambiguity

On Spectral Estimation and Bistatic Clutter Suppression in Radar Systems

Target detection serve as one of the primary objectives in a radar system. From observations, contaminated by receiver thermal noise and interference, the processor needs to determine between target absence or target presence in the current measurements. To enable target detection, the observations are filtered by a series of signal processing algorithms. The algorithms aim to extract information used in subsequent calculations from the observations. In this thesis and the appended papers, we investigate two techniques used for radar signal processing; spectral estimation and space-time adaptive processing.\ua0In this thesis, spectral estimation is considered for signals that can be well represented by a parametric model. The considered problem aims to estimate frequency components and their corresponding amplitudes and damping factors from noisy measurements. In a radar system, the problem of gridless angle-Doppler-range estimation can be formulated in this way. The main contribution of our work includes an investigation of the connection between constraints on rank and matrix structure with the accuracy of the estimates.Space-time adaptive processing is a technique used to mitigate the influence of interference and receiver thermal noise in airborne radar systems. To obtain a proper mitigation, an accurate estimate of the space-time covariance matrix in the currently investigated cell under test is required. Such an estimate is based on secondary data from adjacent range bins to the cell under test. In this work, we consider airborne bistatic radar systems. Such systems obtains non-stationary secondary data due to geometry-induced range variations in the angle-Doppler domain. Thus, the secondary data will not follow the same distribution as the observed snapshot in the cell under test. In this work, we present a method which estimates the space-time covariance matrix based upon a parametric model of the current radar scenario. The parameters defining the scenario are derived as a maximum likelihood estimate using the available secondary data. If used in a detector, this approach approximately corresponds to a generalized likelihood ratio test, as unknowns are replaced with their maximum likelihood estimates based on secondary data

Space-time reduced rank methods and CFAR signal detection algorithms with applications to HPRF radar

In radar applications, the statistical properties (covariance matrix) of the interference are typically unknown a priori and are estimated from a dataset with limited sample support. Often, the limited sample support leads to numerically ill-conditioned radar detectors. Under such circumstances, classical interference cancellation methods such as sample matrix inversion (SMI) do not perform satisfactorily. In these cases, innovative reduced-rank space-time adaptive processing (STAP) techniques outperform full-rank techniques. The high pulse repetition frequency (HPRF) radar problem is analyzed and it is shown that it is in the class of adaptive radar with limited sample support. Reduced-rank methods are studied for the HPRF radar problem. In particular, the method known as diagonally loaded covariance matrix SMI (L-SMI) is closely investigated. Diagonal loading improves the numerical conditioning of the estimated covariance matrix, and hence, is well suited to be applied in a limited sample support environment. The performance of L-SMI is obtained through a theoretical distribution of the output conditioned signal-to-noise ratio of the space-time array. Reduced-rank techniques are extended to constant false alarm rate (CFAR) detectors based on the generalized likelihood ratio test (GLRT). Two new modified CFAR GLRT detectors are considered and analyzed. The first is a subspace-based GLRT detector where subspace-based transformations are applied to the data prior to detection. A subspace transformation adds statistical stability which tends to improve performance at the expense of an additional SNR loss. The second detector is a modified GLRT detector that incorporates a diagonally loaded covariance matrix. Both detectors show improved performance over the traditional GLRT

Radar Signal Processing for Interference Mitigation

It is necessary for radars to suppress interferences to near the noise level to achieve the best performance in target detection and measurements. In this dissertation work, innovative signal processing approaches are proposed to effectively mitigate two of the most common types of interferences: jammers and clutter. Two types of radar systems are considered for developing new signal processing algorithms: phased-array radar and multiple-input multiple-output (MIMO) radar. For phased-array radar, an innovative target-clutter feature-based recognition approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is proposed to detect moving targets in inhomogeneous clutter. Moreover, a new ground moving target detection algorithm is proposed for airborne radar. The essence of this algorithm is to compensate for the ground clutter Doppler shift caused by the moving platform and then to cancel the Doppler-compensated clutter using MTI filters that are commonly used in ground-based radar systems. Without the need of clutter estimation, the new algorithms outperform the conventional Space-Time Adaptive Processing (STAP) algorithm in ground moving target detection in inhomogeneous clutter. For MIMO radar, a time-efficient reduced-dimensional clutter suppression algorithm termed as Reduced-dimension Space-time Adaptive Processing (RSTAP) is proposed to minimize the number of the training samples required for clutter estimation. To deal with highly heterogeneous clutter more effectively, we also proposed a robust deterministic STAP algorithm operating on snapshot-to-snapshot basis. For cancelling jammers in the radar mainlobe direction, an innovative jamming elimination approach is proposed based on coherent MIMO radar adaptive beamforming. When combined with mutual information (MI) based cognitive radar transmit waveform design, this new approach can be used to enable spectrum sharing effectively between radar and wireless communication systems. The proposed interference mitigation approaches are validated by carrying out simulations for typical radar operation scenarios. The advantages of the proposed interference mitigation methods over the existing signal processing techniques are demonstrated both analytically and empirically

Comparison of adaptive radar algorithms : transformed SMI, eigencanceler, and SMI

Advanced airborne radars must perform target detection in the presence of interference and heavy clutter. In many applications, the practical usefulness of adaptive arrays is limited by their convergence rate. In this paper, we first analyze the performance of the SMI method. Then, two other methods, the transformed SMI and the eigencanceler, both based on the principle component inversion (PCI) technique, are described and analyzed by simulation. It is shown by simulation based comparison that the transformed SMI and the Eigencanceler outperform the SMI method. It is also shown that the transformed SMI and the eigencanceler has higher convergence rate in terms of output signal-to-noise ratio than the SMI, specially for short data record sizes. It is concluded that the transformed SMI and the eigencanceler are good alternatives to the SMI method when data set available is small

Signal processing for airborne bistatic radar

The major problem encountered by an airborne bistatic radar is the suppression of bistatic clutter. Unlike clutter echoes for a sidelooking airborne monostatic radar, bistatic clutter echoes are range dependent. Using training data from nearby range gates will result in widening of the clutter notch of STAP (space-time adaptive processing) processor. This will cause target returns from slow relative velocity aircraft to be suppressed or even go undetected. Some means of Doppler compensation for mitigating the clutter range dependency must be carried out. This thesis investigates the nature of the clutter echoes with different radar configurations. A novel Doppler compensation method using Doppler interpolation in the angle-Doppler domain and power correction for a JDL (joint domain localized) processor is proposed. Performing Doppler compensation in the Doppler domain, allows several different Doppler compensations to be carried out at the same time, using separate Doppler bins compensation. When using a JDL processor, a 2-D Fourier transformation is required to transform space-time domain training data into angular-Doppler domain. Performing Doppler compensation in the spacetime domain requires Fourier transformations of the Doppler compensated training data to be carried out for every training range gate. The whole process is then repeated for every range gate under test. On the other hand, Fourier transformations of the training data are required only once for all range gates under test, when using Doppler interpolation. Before carrying out any Doppler compensation, the peak clutter Doppler frequency difference between the training range gate and the range gate under test, needs to be determined. A novel way of calculating the Doppler frequency difference that is robust to error in pre-known parameters is also proposed. Reducing the computational cost of the STAP processor has always been the desire of any reduced dimension processors such as the JDL processor. Two methods of further reducing the computational cost of the JDL processor are proposed. A tuned DFT algorithm allow the size of the clutter sample covariance matrix of the JDL processor to be reduced by a factor proportional to the number of array elements, without losses in processor performance. Using alternate Doppler bins selection allows computational cost reduction, but with performance loss outside the clutter notch region. Different systems parameters are also used to evaluate the performance of the Doppler interpolation process and the JDL processor. Both clutter range and Doppler ambiguity exist in radar systems operating in medium pulse repetitive frequency mode. When suppressing range ambiguous clutter echoes, performing Doppler compensation for the clutter echoes arriving from the nearest ambiguous range alone, appear to be sufficient. Clutter sample covariance matrix is estimated using training data from the range or time or both dimension. Investigations on the number of range and time training data required for the estimation process in both space-time and angular-Doppler domain are carried out. Due to error in the Doppler compensation process, a method of using the minimum amount of range training data is proposed. The number of training data required for different clutter sample covariance matrix sizes is also evaluated. For Doppler interpolation and power correction JDL processor, the number of Doppler bins used can be increased, to reduce the amount of training data required, while maintaining certain desirable processor performance characteristics

A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space-Time Adaptive Processing

We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments