Airborne Phased Array Radar Research Articles

Intelligent decision-making algorithm for airborne phased array radar search tasks based on a hierarchical strategy framework

To address the guided search task of airborne phased array radar in the scenarios of large airspace with widespread distribution of cluster targets in Beyond Visual Range (BVR) air combat, a hierarchical strategy framework based on deep reinforcement learning is proposed to guide different stages of search tasks. Firstly, an airspace set-covering model and a radar parameter optimization model for the guided search task of cluster targets are established. Secondly, the hierarchical strategy framework including upper-level and lower-level strategies is constructed based on the above models. Finally, the happo-rgs algorithm is proposed for feature extraction from Markov continuous observation sequences, to enhance the training effectiveness and improve the algorithm convergence speed. Simulation results show that the trained agent can make precise autonomous decisions rapidly based on airspace-target covering situation and target guidance information which significantly improves the radar search performance in the forementioned scenarios compared to traditional algorithms.

Window Function Design for Asymmetric Beampattern Synthesis and Applications

This letter proposes a novel window function design to synthesize asymmetric beampatterns while minimizing the gain loss for clutter/interference suppression as required in practical applications, such as airborne phased array radar (APAR). Compared with beampattern synthesis in terms of designing the weight vector directly, the proposed method can achieve asymmetric sidelobes for multiple steering directions resort to designing one window while circumventing repetitive computations. To tackle the resultant non-convex optimization problem, an iterative alternating optimization (IAO) algorithm is introduced where each iteration involves multiple subproblems that can be solved via either convex approximation (CA) approach or closed-form solutions. Finally, numerical simulations are provided to verify the effectiveness of the proposed method comparing with the classical adjustable window functions (CAWFs).

Optimal Search Strategy of Low-Altitude Target for Airborne Phased Array Radar Using Digital Elevation Model

The search for low-altitude targets becomes a more challenging task for airborne phased array radar, due to the limited system resources and serious ground clutter. In this paper, an optimal search strategy using the digital elevation model (DEM) is proposed to allow for fast and high-probability search for low-altitude targets. First, a clutter-free detection region is formed by introducing the DEM. Compared with the traditional methods, the detection probability of the low-altitude targets can be improved. Second, the search problem is transformed into a constrained multi-objective optimization problem. The proposed strategy can simultaneously minimize the search time and maximize the target detection probability. Finally, two intelligent evolutionary algorithms are compared to verify the proposed strategy, and the proposed strategy is also testified by utilizing a real DEM data of Dujiangyan, Sichuan, China. Simulated results indicate that the proposed optimal search framework improves the detection probability and reduces time cost for low-altitude targets.

Incoherent Integration Detection Method of Airborne Phased Array Radar in a Multipath Environment

Airborne phased array radar (PAR) suffers from multipath problems when flying over a calm sea surface. The existence of a multipath phenomenon will cause the electromagnetic echo of the same target to be reflected back to the airborne PAR from two paths, namely, direct path (DP) and multipath. Compared with the ground-based radar, the target echo received by airborne PAR in the multipath environment has two important characteristics: one is that the DP signal and the multipath signal exist in different range bins, and the other is that the radar cross section (RCS) in the DP direction may be smaller than that in the multipath direction. Considering these two characteristics, this paper first proposes a target pairing algorithm for matching the DP range and multipath range of the same target in signal detection, and then, combined with the cell-averaging constant false alarm rate (CA-CFAR) detection model, an incoherent integration detection method for airborne PAR in the multipath environment is proposed. In the target pairing process, the geometric structure relationship of the airborne PAR model can be fully utilized. After a successful target pairing process, the energy of the multipath signal will be incoherently accumulated into the corresponding DP range bin, so as to improve the probability of DP range bin data passing the detection threshold. In essence, the proposed method makes full use of multipath energy to improve the detection capability of airborne PAR in the multipath environment. Finally, the detection probability of the proposed method is given, and the detection performance is analyzed.

Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar

In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filter designed to suppress platform motion-induced clutter Doppler spread. Interference is characterized by a covariance matrix. In the case of a forward-looking airborne radar, the clutter Doppler is dependent on range. Clutter Doppler dependency on the range renders the training cells heterogeneous. The heterogeneity effects are particularly prominent in the near range bins. Non-homogeneous training cells have a deleterious effect on STAP performance. In this study, we propose an adaptive Doppler compensation to mitigate the degraded STAP performance in the near range bins. The adaptivity feature circumvents the need for the availability of radar parameters in real-time. The real time implementation of STAP is impeded by requirements of a large number of training samples and covariance matrix inversion. Therefore, there is a dire need to devise a framework to detect and estimate target parameters within the STAP. In this regard, we propose an efficient STAP algorithm to detect and estimate target parameters. STAP weights are applied to the input data to obtain a 3D array. The range projection of the 3D array is utilized to detect and estimate the range of the target, while the angle–Doppler projection is used to estimate spatial and temporal parameters of the target. Most of the literature on STAP is geared towards a known covariance matrix. The assumption of a known covariance matrix may degrade STAP performance because of the inherent mismatches between the actual and assumed target steering vectors. In this study, we estimate the covariance matrix based on the synthetic data generated from a model of an airborne phased array radar. The developed STAP algorithms closely mimic a real-time implementation scheme in an airborne radar platform. The results of the proposed algorithm are validated through target parameter estimation and STAP metrics on synthetic data.

STAP Optimization of Airborne Phased Array Radar in Nonuniform Environment Based on EFA Algorithm

EFA (extended factored approach) algorithm is the main method of space-time adaptive processing technology (STAP) for airborne phased array radar, but it is faced with many problems, such as large number of samples and large amount of calculation. Therefore, this paper uses a method of spatial data dimensionality reduction processing based on cyclic iterative calculation to optimize its STAP. The final experimental results show that, after spatial data dimensionality reduction processing optimization, the STAP performance of EFA algorithm is further expanded in the range of sample number adaptation; especially in the case of small sample number, the optimized STAP performance has been basically close to the ideal compared with other optimization schemes; tap performance also proves that the optimization scheme in this paper has better convergence speed and STAP performance.

Robust knowledge‐aided sparse recovery STAP method for non‐homogeneity clutter suppression

Conventional space–time adaptive processing (STAP) methods would suffer severely performance loss in the complex clutter environment of an airborne-phased array radar, especially when the estimated clutter covariance matrix (CCM) is corrupted by the interference targets (outliers). In order to improve the clutter suppression in a practical complex clutter, a robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression is proposed. In the proposed method, the spectral profiles of the clutter and outliers are firstly estimated by sparse recovery processing. Then based on the system prior parameters, the clutter mask is constructed to select the space–time steering vectors corresponding to the clutter components. Afterwards the clutter suppression is achieved based on the clutter subspace obtained from the selected space–time steering vectors. Since the clutter and outlier profiles are effectively estimated and distinguished by the knowledge-aided sparse recovery processing, robust clutter subspace estimation can be achieved for clutter suppression. Through the simulated and actual airborne-phased array radar data, it is verified that the proposed method can effectively improve the STAP performance in a non-homogeneous environment.

Polarisation‐space‐time adaptive processing for heterogeneous clutter suppression of airborne‐phased array radar

Conventional space-time adaptive processing (STAP) achieves clutter suppression by combining spatial signals and the temporal pulses. However, extreme slow-moving target cannot be effectively detected by space-time joint filtering in ambiguous and non-broadside clutter environment for airborne-phased array radar. By increasing the polarisation information in clutter suppression, improved moving target detection performance can be obtained by the polarisation-space-time adaptive processing (P-STAP). In this study, the performance of P-STAP for airborne-phased array radar is analysed. The polarised signal model of airborne-phased array radar is firstly established and then clutter suppression principle of P-STAP is derived in detailed. Afterwards, the performance of P-STAP is investigated and compared with conventional STAP in ambiguous clutter environment. It is verified that the P-STAP can outperform the conventional STAP for slow moving target detection by three-domain joint processing, especially for ambiguous and non-broadside clutter environment for airborne-phased array radar.

Reduced Dimension STAP Based on Sparse Recovery in Heterogeneous Clutter Environments

For airborne-phased array radar systems, space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in the strong clutter background. However, practical application environments are always heterogeneous and have offered a severe challenge to the implementation of STAP. To address this reality, a data-dependent reduced dimension STAP approach based on sparse recovery (SR) is proposed in this paper. To take a full account of the heterogeneous environments, we consider the extremely heterogeneous case in which only one single snapshot is available. There is no doubt that, compared with the clutter covariance matrix (CCM) calculated directly by a single snapshot, the SR technique can provide a more accurate estimate. However, we should come to realize that this estimation is not accurate enough to adaptive processing according to the presented simulation results in lots of literature, and it has been demonstrated that the performance of traditional SR-based STAP degrades dramatically when only a snapshot is available. In the proposed approach, the CCM estimated by SR technique is utilized to design the reduced dimension transformation matrix rather than to calculate adaptive weights as the traditional SR-based STAP. The relatively accurate CCM can provide better support for the design of reduced dimension transformation matrix. From the simulation results, the proposed approach can achieve great performance of clutter suppression and target detection with only a single snapshot compared with several typical STAP algorithms. It is worthwhile pointing out that the proposed approach can also be applied when multiple snapshots are available and the performance improves with increasing number of available snapshots.

Discrete Interference Suppression Method Based on Robust Sparse Bayesian Learning for STAP

Discrete interference influences the performance of existing space-time adaptive processing methods in practical scenarios. In order to effectively suppress discrete interference in real clutter environment, a discrete interference suppression method based on robust sparse Bayesian learning (SBL) is proposed for airborne phased array radar. In the proposed method, the estimation of spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are carried out iteratively. During one iteration, the prominent components of clutter and discrete interference in the spatial-temporal plane are first estimated by SBL, and then the overcomplete dictionary is calibrated by calculating the error matrix. Because of the robust estimation of spatial-temporal spectral distribution, both the discrete interference and the homogeneous clutter profiles can be effectively suppressed with a small number of space-time data. The effectiveness of the proposed method is verified in the nonhomogeneous environment by utilizing simulated and actual airborne phased array radar data.

Airborne polarimetric Doppler weather radar: trade-offs between various engineering specifications

Abstract. NCAR EOL is investigating potential configurations for the next-generation airborne phased array radar (APAR) that is capable of retrieving dynamic and microphysical characteristics of clouds and precipitation. The APAR will operate at C band. The APAR will use the electronic scanning (e-scan) feature to acquire the optimal number of independent samples for recording research-quality measurements. Since the airborne radar has only a limited time for collecting measurements over a specified region (moving aircraft platform ∼ 100 m s−1), beam multiplexing will significantly enhance its ability to collect high-resolution, research-quality measurements. Beam multiplexing reduces errors in radar measurements while providing rapid updates of scan volumes. Beamwidth depends on the size of the antenna aperture. Beamwidth and directivity of elliptical, circular, and rectangular antenna apertures are compared and radar sensitivity is evaluated for various polarimetric configurations and transmit–receive (T/R) elements. In the case of polarimetric measurements, alternate transmit with alternate receive (single-channel receiver) and simultaneous reception (dual-channel receiver) is compared. From an overall architecture perspective, element-level digitization of T/R module versus digital sub-array is considered with regard to flexibility in adaptive beamforming, polarimetric performance, calibration, and data quality. Methodologies for calibration of the radar and removing bias in polarimetric measurements are outlined. The above-mentioned engineering options are evaluated for realizing an optimal APAR system suitable for measuring the high temporal and spatial resolutions of Doppler and polarimetric measurements of precipitation and clouds.

Robust Space Time Adaptive Processing in Heterogeneous Environment

Airborne phased array radar receives strong clutter when it detects ground moving target, and clutter seriously degrades the performance of target detection. Space time adaptive processing (STAP) is able to suppress clutter, while it needs training samples to estimate clutter covariance matrix. In heterogeneous interference environment, the covariance matrix cannot be well estimated and the performance of clutter suppression degrades. This paper proposes a method of covariance matrix estimation to improve the performance of STAP in heterogeneous interference environments. The method first discards training samples which may be contaminated by target signals. Then, the correlation coefficients of the cell under test and the remaining samples are estimated, and the coefficients are used as weighting coefficients to measure the contributions of the corresponding samples to the estimation of covariance matrix. The accuracy of the estimated covariance matrix can be improved significantly, and thus results in an evident performance improvement of STAP in heterogeneous environments. Experimental results demonstrate the effectiveness of the proposed method.

Linear Regression Based Clutter Reconstruction for STAP

Space-time adaptive processing (STAP) is supposed to be a crucial technique for improving target detection performance in a strong clutter background for airborne phased array radar systems. In this paper, we consider the extremely heterogeneous case, i.e., the number of available training samples is limited to one. The sparse recovery (SR) technique is first utilized to obtaining the independent clutter patches. Contrary to traditional SR STAP which estimates the clutter covariance matrix (CCM) with these clutter patches, the proposed approach will estimate the ‘clutter ridge’ based on linear regression by making use of these clutter patches. With the prior knowledge of number of receiver elements, a more accurate estimation of CCM is obtained. From the simulation results, the proposed approach can achieve a great performance enhancement of clutter suppression with only one training sample compared with conventional SR based STAP algorithms. Even for the cases where amplitude and phase errors are consider, the proposed approach can be superior to traditional SR STAP about 5~10 dB.

Compressive sensing-based wind speed estimation for low-altitude wind-shear with airborne phased array radar

An important issue in low-altitude wind-shear detection is to estimate the wind speed of wind field. In this paper, a novel method for wind speed estimation with airborne phased array radar is proposed by combining space time adaptive processing and compressive sensing. The proposed method is able to achieve accurate wind speed estimate in the condition of limited number of sampling pulses, as demonstrated by numerical examples.

Fast STAP Method Based on PAST with Sparse Constraint for Airborne Phased Array Radar

Conventional space-time adaptive processing (STAP) is difficult to effectively suppress nonhomogeneous clutter in practical airborne phased array radar systems owing to the requirements of large independent and identically distributed training samples and the high complexity involved in the inversion of a high dimensional matrix. In order to improve the clutter suppression performance with a small training sample supports, a fast STAP method based on projection approximation subspace tracking (PAST) with sparse constraint is proposed in this paper. In the proposed method, based on the low-rank property of the clutter covariance matrix, a sparse constraint is imposed in the cost function of PAST, and the adaptive weight vector is then derived iteratively. Because of the sparse constraint in PAST, the proposed method provides a more robust and stable estimation of the clutter subspace, especially when only a small set of training samples is available. Moreover, the proposed method not only achieves lower computational complexity compared with existing sparsity constrained STAP methods, but also better performance and faster convergence compared with conventional STAP methods without sparse constraint. The effectiveness of the proposed method is verified based on simulated and actual airborne phased array radar data.

Search optimization for minimum load under detection performance constraints in multi-function phased array radars

This paper presents a solution procedure of surveillance parameter optimization to achieve minimum radar load while ensuring desired one-off and cumulative probabilities of detection in a multi-function phased array radar. The key approach is to convert the nonlinear optimization on four search parameters – beam width, beam spacing ratio, dwell time, and frame time – into a scalar optimization on signal-to-noise ratio by a semi-analytic procedure based on subproblem decomposition, which improves computational efficiency of the optimization process as well as facilitates physical interpretation of the optimized parameters. The efficacy of the proposed solution approach is verified with theoretical analysis and numerical case study on an airborne phased array radar.

The next generation airborne polarimetric Doppler weather radar

Abstract. Results from airborne field deployments emphasized the need to obtain concurrently high temporal and spatial resolution measurements of 3-D winds and microphysics. A phased array radar on an airborne platform using dual-polarization antenna has the potential for retrieving high-resolution, collocated 3-D winds and microphysical measurements. Recently, ground-based phased array radar (PAR) has demonstrated the high time-resolution estimation of accurate Doppler velocity and reflectivity of precipitation and clouds when compared to mechanically scanning radar. PAR uses the electronic scanning (e-scan) to rapidly collect radar measurements. Since an airborne radar has a limited amount of time to collect measurements over a specified sample volume, the e-scan will significantly enhance temporal and spatial resolution of airborne radar observations. At present, airborne weather radars use mechanical scans, and they are not designed for collecting dual-polarization measurements to remotely estimate microphysics. This paper presents a possible configuration of a novel airborne phased array radar (APAR) to be installed on an aircraft for retrieving improved dynamical and microphysical scientific products. The proposed APAR would replace the aging, X-band Electra Doppler radar (ELDORA). The ELDORA X-band radar's penetration into precipitation is limited by attenuation. Since attenuation at C-band is lower than at X-band, the design specification of a C-band airborne phased array radar (APAR) and its measurement accuracies are presented. Preliminary design specifications suggest the proposed APAR will meet or exceed ELDORA's current sensitivity, spatial resolution and Doppler measurement accuracies of ELDORA and it will also acquire dual-polarization measurements.

Cognitive Space-Time Transmit Pattern Design

This paper provides a cognitive space-time (angle-Doppler) transmit pattern design method for mitigating clutter effect. This pattern takes full advantage of degrees-of-freedom (DOF) on transmit, which can preserve the maximum response for the target of interest while prenulling the mainlobe clutter on transmit, potentially simplifying processing on receiver and reducing the requirement for receiver dynamic range. The output signal-to-clutter-and-noise ratio (SCNR) can be improved significantly after applying the cognitive space-time transmit pattern to airborne-phased array radar. In addition, the traditional transmit antenna pattern and the proposed cognitive scheme are compared in terms of the probability of target detection. Simulations are conducted to demonstrate proof-of-concept.

Two-dimensional pulse-to-pulse canceller applied in multiple-input–multiple-output radar

Since the spectrum of the ground clutter in the ground-based pulse radar is spatially–temporally decoupled, a simple pulse-to-pulse canceller is often used as a moving target indication filter for suppressing the ground clutter near the zero-Doppler frequency. In contrast, in the airborne phased-array radar, which is a typical single-input–multiple-output radar, the spectrum of the ground clutter is spatially–temporally coupled each other, and the two-dimensional pulse-to-pulse canceller (TDPC) for ground clutter cancellation has been developed by using the ground clutter model in the spatial–temporal domain. Based on the ground clutter model of multiple-input–multiple-output (MIMO) radar, the authors can extend the existing TDPC to more general form that can suppress the ground clutter in the MIMO radar. As the MIMO TDPC can be calculated offline by utilising the geometry knowledge of the ground clutter of MIMO radar, it can be used as an efficient and convenient ground clutter pre-filtering tool.

Elevation Adaptive Algorithm for Short-Range Clutter Suppression in Airborne Phased Array Radar

摘要: 该文提出了一种基于高阶累积量矩阵组正交联合对角化的高分辨方位估计方法。该方法构造了一组高阶累积量矩阵共同来辨识阵列流型矩阵的列空间，进而进行DOA估计。并通过对高阶累积量矩阵组进行联合对角化，得到联合对角化矩阵和对角化后的矩阵组，并重新定义了空间谱。新方法可以处理相干声源，适用于有色噪声环境，且较仅使用单个高阶累积量矩阵的算法具有更高的分辨力，更低的均方根误差和更高的鲁棒性。 关键词: 信号处理; 水声信号; 高阶累积量矩阵; 正交联合对角化; Jacobi旋转; 方位估计