Forward-looking airborne radar clutter spectrum with amplitude and phase errors

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

It is known that the knowledge of sparsity of data is valuable to improve the convergence of space-time adaptive processing (STAP) algorithm in airborne radar. Recently, a STAP algorithm was proposed where the sparsity of beam-Doppler pattern is exploited to achieve good performance. However, a matrix inversion operator is involved in this algorithm, which results in high computational burden. In this paper, we present a novel STAP algorithm which is free of matrix inversion. Precisely, the filter weight vector is first expressed by introducing an intermediate vector. The intermediate vector is then derived iteratively by employing CG techniques under some simple assumptions. The filter weight vector can thus be computed by the derived intermediate vector. Therefore, the proposed algorithm avoids the matrix inversion and achieves low computational complexity. Numerical simulation results are provided to illustrate the performance and superiority of the proposed algorithm.

In this paper, a space-time clutter model with non-Gaussian statistics for varying airborne bistatic radar scenarios is presented, which can be widely employed to assess the performance of bistatic space-time adaptive processing (STAP) algorithms in realistic clutter environments of interest. To extract the fundamental properties of bistatic clutter, i.e. the range dependency of bistatic clutter spectrum and the clutter power variation in range-angle cells, a new approach of coordinate system conversion is initiated into formulating bistatic geometrical model by us, and the bistatic non-Gaussian amplitude clutter representation method based on compound model is introduced. The bistatic configuration parameters of Multi-Channel Airborne Radar Measurement (MCARM) experiment and the experimental non-Gaussian clutter data are used to validate the provided models, and the results manifest that the proposed bistatic clutter model is reliable and apt to accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.

The performance of the optimal and reduced-dimension Space-Time Adaptive Processing (STAP) algorithms for the airborne phased array radar is investigated in this paper. The clutter suppression in the mainbeam and the sidelobe clutter regions and the computation load are analyzed and compared between the optimal and two kinds of typical reduced-dimension STAP algorithms, i.e. the 2-D temporal-spatial adaptive algorithm (TSA) and the m-bins Doppler transform-space adaptive processing (mDT-SAP) algorithm. Based on the performance analysis of these STAP algorithms, a new clutter suppression strategy is proposed with consideration of actual clutter environment and the spatial channel and temporal pulse restricted system, which can detect the target more effectively and implement more easily for the airborne phased array radar.

This research presents two new space-time adaptive processing (STAP) algorithms; a two-dimensional non-statistical method and a hybridisation of this approach with statistically based methods. The non-statistical algorithm developed here allows filtering of uncorrelated interference, such as discrete interferers, within the range cell of interest. However, the performance of these algorithms in homogeneous correlated interference scenarios is inherently inferior to traditional statistical STAP algorithms. The proposed hybrid algorithm alleviates this drawback by implementing a second stage of statistical adaptive processing. This paper illustrates the advantages of using a two stage adaptive process to combine the direct data domain and statistical algorithms. The work presented in this paper brings together two different aspects of STAP research: statistical and direct data domain processing. In doing so, this research fulfils an important need in the context of practical STAP processing.

The clutter spectrum depends highly on range for forward-looking radars, which results in heterogeneous ground clutter in space-time adaptive processing (STAP). A new method for the range-dependence of clutter spectrum is proposed. The method firstly obtains a transform matrix by fitting the spatial steering vector of the range gate under test with that of secondary range gate. The transform matrix is then applied to spatial data at a given Doppler gate to compensate clutter spectrum, thus achieving an identical clutter spectrum. The conventional STAP could exploit the compensated data for the clutter suppression. Simulation results show a good improvement in processor performance. (4 pages)

Low sidelobe levels may be a crucial factor in the success of achieving high gain using linear receiving arrays, since the level of the sidelobes determines the degree to which noisy nearby sources can be prohibited from interfering with the detection of a weak source on a different azimuth. In real world experiments at sea, one frequently operates with an array which has not only errors in amplitude and phase between channels but also dead elements. The effect of random errors in both amplitude and phase is to yield a sidelobe level of the average beam pattern which is the sum of the error-free pattern and an additional level due to the random errors. The sidelobe level due to the random errors depends upon the details of the amplitude shading function, the distribution function from which the random errors are drawn, and increases with the mean square value of the errors. Thus, given rms values of amplitude and phase errors, there is a practical limit to which the sidelobes can be reduced by amplitude shading the array. The effect of element failure on the sidelobe level can be drastic and depends on the sum of the amplitude weights of the dead elements. Thus, a failure of a highly-weighted element will have a greater effect on the sidelobe level than the failure of two or three whose combined weights are less. [Work supported by NAVMAT.]

The amplitude and phase errors deteriorate the low-sidelobe level characteristic of phased array antenna, which degrades radar performance seriously. Therefore an improved calibration method of amplitude and phase errors based on diagonal loading is proposed to address the problem of amplitude and phase errors in this paper. Initially, the mathematical models of amplitude and phase errors are developed and the impact of amplitude and phase errors is analyzed. It is found that although the interference can be suppressed when there is amplitude and phase errors in the array, the low-sidelobe level characteristic of adaptive antenna is badly deteriorated. An improved calibration method of amplitude and phase errors based on diagonal loading is proposed. Compared with traditional calibration method, proposed method can reduce the sidelobe level efficiently. At last simulations were carried out to verify the effectiveness of proposed method.

Space-time adaptive monopulse processing for airborne radar in non-homogeneous environments

On the quadrature accuracy of in-phase coupled quadrature LC oscillator

A new reduced‐rank (RR) space‐time adaptive processing (STAP) algorithm based on multistage selections of angle‐Doppler filters is proposed in the form of a generalised sidelobe canceller. First, two types of the RR auxiliary angle‐Doppler filters are designed based on the discrete Fourier basis functions. Then, a novel multistage method is implemented to select the angle‐Doppler filters by using the cross‐correlation observed at the current stage and the residual output observed at the previous stage. Finally, the asymptotic statistic performance of target detection is evaluated in terms of the conventional framework of STAP for the cases of both known and unknown covariance, which shows that the proposed algorithm is capable of providing the constant false alarm rate. Numerical examples are provided, and they demonstrate that the proposed algorithm is able to offer better performance than the existing sparsity‐based and the conventional reduced‐dimension algorithms under the intrinsic clutter motion and array gain and phase errors.

Antenna arrays with space-time adaptive processing (STAP) are commonly utilized to allow the reception of signals in harsh interference environments. The primary applications of STAP have been radar and communications systems, where the STAP weights are optimized for output signal-to-interference-plus-noise ratio (SINR); however, there is increasing demand for STAP in time-of-arrival (TOA) estimation applications, such as global navigation satellite system (GNSS) receivers. It is understood that TOA estimation performance is primarily dependent on receiver postcorrelation carrier-to-noise ratio (C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ). In this study, the distinction between SINR and C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is established, and a performance analysis of the common STAP algorithms is performed in the presence of interference. It is demonstrated that output SINR is an inadequate indication of GNSS receiver C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> performance. As a result, caution should be used when measuring the relative performance of different STAP algorithms in GNSS applications. Consequently, the present work develops a novel STAP algorithm which maximizes C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Additionally, the performance of common STAP algorithms is analyzed in the context of this performance bound, and it is demonstrated that, in many cases, their performance is near optimal.

Optically pumped magnetometer (OPM) has gained popularity as a viable alternative to the superconducting quantum interference device (SQUID) for biomagnetic measurements. However, the presence of amplitude and phase errors in the OPM can degrade the signal quality of, e.g., magnetoencephalography (MEG) signals. This study presents a dual-PI closed-loop control (DPCC) method aimed at suppressing amplitude and phase errors of the OPM when operated in the spin-exchange relaxation-free (SERF) regime, thereby enhancing MEG localization accuracy. The main sources of the amplitude and phase errors in the OPM are identified through a theoretical analysis of the inertial element characteristics and static residual magnetic fields. The experimental results demonstrate that the utilization of DPCC for the OPM reduces the amplitude and phase errors in static MEG measurements by 94.2% and 95.6%, respectively. At a residual magnetic field of 6.5 nT, the amplitude and phase errors are further suppressed by 97.8% and 87.2%, respectively. Furthermore, the fluctuation of the residual magnetic field is reduced by a factor of 25, significantly enhancing the robustness of the OPM. This straightforward and effective arrangement of the OPM holds promise in the field of biomagnetism and is expected to enhance the localization and calibration accuracy of OPM-based MEG (OPM-MEG) systems.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or &#x201C;zero-bandwidth,&#x201D; where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

Space-time adaptive processing (STAP) algorithms typically consist of a data transformation step to reduce the number of degrees of freedom and a sampling step wherein radar returns from adjacent range bins are used to estimate interference statistics. The reduction in the number of degrees of freedom, inadequate sample support, presence of target in sampled data, and range dependence of interference are some of the main reasons for STAP performance loss. In this paper, we present an approach to target detection and localization that mitigates these performance losses using the well-known stochastic realization algorithm from system identification theory. We first identify a state space model from the radar return data in range-pulse domain for a given range bin, and then perform detection and localization using the identified state space matrices. As interference statistics are not directly computed and since there is no sampling from adjacent range bins, this approach is more robust to sample support issues, target in training and range dependence of clutter. A numerical comparison of the approach with beam-space post-Doppler STAP using simulated data is given.