Signal-to-clutter-plus-noise Ratio Research Articles

Data‐driven target localization using adaptive radar processing and convolutional neural networks

AbstractLeveraging the advanced functionalities of modern radio frequency (RF) modeling and simulation tools, specifically designed for adaptive radar processing applications, this paper presents a data‐driven approach to improve accuracy in radar target localization post adaptive radar detection. To this end, we generate a large number of radar returns by randomly placing targets of variable strengths in a predefined area, using RFView®, a high‐fidelity, site‐specific, RF modeling & simulation tool. We produce heatmap tensors from the radar returns, in range, azimuth [and Doppler], of the normalized adaptive matched filter (NAMF) test statistic. We then train a regression convolutional neural network (CNN) to estimate target locations from these heatmap tensors, and we compare the target localization accuracy of this approach with that of peak‐finding and local search methods. This empirical study shows that our regression CNN achieves a considerable improvement in target location estimation accuracy. The regression CNN offers significant gains and reasonable accuracy even at signal‐to‐clutter‐plus‐noise ratio (SCNR) regimes that are close to the breakdown threshold SCNR of the NAMF. We also study the robustness of our trained CNN to mismatches in the radar data, where the CNN is tested on heatmap tensors collected from areas that it was not trained on. We show that our CNN can be made robust to mismatches in the radar data through few‐shot learning, using a relatively small number of new training samples.

A novel non-convex penalty function-based STAP algorithm for airborne passive radar systems

In conventional sparse Space-Time Adaptive Processing (STAP) algorithms, the l0-norm is often used instead of the l1-norm to relax convexity constraints. The approach presented in this paper can be mathematically reformulated as a linear problem, which can be efficiently solved using various convex optimization techniques. While this method effectively circumvents the NP-Hard complexity associated with the l0-norm, it does come with certain potential challenges. For example, the observation matrix must meet the criteria of the Restricted Isometry Property (RIP) and other stringent conditions. In this research, a novel STAP algorithm is introduced, which is based on a non-convex penalty function. This innovative algorithm substitutes the traditional l0-norm with a custom-designed non-convex penalty function and is applied to the Direct Filter Processor (DFP), solved using the recursive least squares (RLS) algorithm. The resulting algorithm, known as gp-RLS, is demonstrated through Monte Carlo simulations to outperform other l1-based and reduced-rank STAP algorithms in terms of faster convergence, improved signal-to-clutter-plus-noise ratio (SCNR), and enhanced target detection performance.

Minimum detection velocity performance analysis for air moving target detection in a spaceborne surveillance radar system

AbstractDue to the advantages of wide coverage, continuous remote monitoring, and high measurement accuracy, the spaceborne multi‐channel surveillance radar has been widely used for the air moving target detection and tracking. In this paper, the minimum detection velocity (MDV) definition for the aerial target detection performance evaluation in a spaceborne multi‐channel radar system is analysed, where three kinds of MDV definitions based on empirical formula, output signal‐to‐clutter‐plus‐noise ratio (SCNR) loss criterion, and output SCNR criterion are discussed in detail based on theoretical analysis and simulation verification. The analysis results will provide valuable reference for the practical spaceborne radar system designment with the air target detection mode.

A sea clutter suppression algorithm for over-the-horizon radar based on dictionary learning and subspace estimation

Detecting a ship target by over-the-horizon radar (OTHR) is seriously affected by sea clutter. This paper proposes a clutter suppression algorithm based on dictionary learning and subspace estimation (DLSE). First, the K-singular value decomposition (K-SVD) is used for dictionary learning and sparse representation of the sea clutter. Then, the orthogonal matching pursuit (OMP) is used to reconstruct the sea clutter component to suppress the outliers. Finally, the clutter covariance matrix is calculated using the reconstructed clutter spectrum, and the clutter subspace of the covariance matrix is estimated to achieve clutter suppression. In this paper, the K-SVD is improved to preserve the phase information of the complex signal. The sea clutter spectrum, after sparse representation and reconstruction, is not impacted by outliers in neighboring cells. Therefore, the clutter covariance matrix has a better estimate accuracy than existing subspace decomposition algorithm. The adaptive parameter estimate method also enhances the adaptability of the DLSE. The experimental results of real data demonstrate that the DLSE is as sophisticated as existing subspace decomposition algorithms while having a higher output signal-to-clutter-plus-noise ratio (SCNR) and peak-to-side lobe ratio (PSLR), improving about 2-5 dB. Results of outlier suppression attest to the robustness of the DLSE. Under various coherent integration times (CIT) and frequencies, the DLSE has the best clutter suppression performance among the comparison algorithms.

Joint Optimization of Radar and Communications Performance in 6G Cellular Systems

Dual functional radar communication (DFRC) is a promising approach that provides a viable solution for the problem of spectrum sharing between communication and radar applications. This paper studies a DFRC system with multiple communication users (CUs) and a radar target. The goal is to devise beamforming vectors at the DFRC transmitter in such a way that the radar received signal-to-clutter-plus-noise-ratio (SCNR) is maximized while satisfying the minimum data rate requirements of the individual CUs. With regard to clutter, we consider two scenarios based on the possibility of clutter removal. Even though the formulated optimization problems are non-convex, we present efficient algorithms to solve them using convex optimization techniques. Specifically, we use duality theory and Karush-Kuhn-Tucker conditions to show the underlying structure of optimal transmit precoders. In the proposed solution, it is observed that there is no need to transmit separate probing signal for the radar detection in both the considered scenarios. This results in reduction in the number of optimization variables in the problem. Moreover, we make use of the asymptotic equivalence between Toeplitz matrices and Circulant matrices to further reduce the complexity of the proposed algorithm. Finally, numerical results are presented to demonstrate the effectiveness of the proposed algorithms.

Observations of Ionospheric Clutter at Near Equatorial High Frequency Radar Stations

The temporal variation of received clutter and noise at a pair of oceanographic high frequency radars (HFR) operating near the geomagnetic equator in the Republic of Palau is investigated. Oceanographic HFRs process range-gated Doppler spectra from groundwave signals that are backscattered from the ocean’s surface to derive maps of ocean currents. The range performance of the radars exhibited a regular diurnal signal which is determined to be a result of both ionospheric clutter and noise. The increased Clutter plus Noise Floor (C+NF) decreases the Signal to Clutter plus Noise Ratio (SCNR) which, in turn, reduces the range and quality of ocean surface current measurement. Determining the nature and origin of this degradation is critical to QA/QC of existing HFR deployments as well as performance predictions of future installations. Nighttime impacts are most severe and negatively affect ocean surface current measurements as low SCNR is found to extend across the Doppler spectra at all ranges, challenging the ability of HFR to map the ocean surface current. Daytime degradation is less severe and presents itself in a way consistent with independent observations of ionospheric clutter, specifically the diurnal temporal pattern and range where the C+NF features occur. A timeseries analysis of SCNR and C+NF is pursued to understand this relationship using received range-dependent Doppler spectra and C+NF features using image segmentation techniques. Clutter plus noise features are classified into daytime, nighttime, and no-noise feature types. The diurnal structure and variability of these features are examined, and the occurrences of each feature type are calculated. The occurrences are compared with space weather indices including a measure of geomagnetic activity, namely the EE (Equatorial Electro Jet) index (determined from magnetometers measuring the earth’s magnetic field), as well as solar impacts using the F10.7 solar radio clutter index to assess the relationship of ionospheric conditions with HFR ocean surface current measurement.

Ground Clutter Mitigation for Slow-Time MIMO Radar Using Independent Component Analysis

The detection of low, slow and small (LSS) targets, such as small drones, is a developing area of research in radar, wherein the presence of ground clutter can be quite challenging. LSS targets, because of their unusual flying mode, can be easily shadowed by ground clutter, leading to poor radar detection performance. In this study, we investigated the feasibility and performance of a ground clutter mitigation method combining slow-time multiple-input multiple-output (st-MIMO) waveforms and independent component analysis (ICA) in a ground-based MIMO radar focusing on LSS target detection. The modeling of ground clutter under the framework of st-MIMO was first defined. Combining the spatial and temporal steering vector of st-MIMO, a universal signal model including the target, ground clutter, and noise was established. The compliance of the signal model for conducting ICA to separate the target was analyzed. Based on this, a st-MIMO-ICA processing scheme was proposed to mitigate ground clutter. The effectiveness of the proposed method was verified with simulation and experimental data collected from an S-band st-MIMO radar system with a desirable target output signal-to-clutter-plus-noise ratio (SCNR). This work can shed light on the use of ground clutter mitigation techniques for MIMO radar to tackle LSS targets.

Joint Design Method of Transmit-Receive for Airborne MIMO Radar Based on Feasible Point Pursuit

Consider the moving target detection performance degradation of airborne multiple-input multiple-output (MIMO) radar in the presence of inaccurate target prior information. This paper proposes a joint design method of transmit waveform and receive filter bank of airborne MIMO radar based on feasible point pursuit successive convex approximation (FPP-SCA). Firstly, a set of receive filter banks is designed in the region where the target may appear on the angle-Doppler plane, and the worst-case output signal-to-clutter-plus-noise ratio (SCNR) is maximized as the optimization criterion. Secondly, considering the energy constraint and similarity on the transmit waveform, the maximin joint design problem is formulated to improve the robustness of the MIMO space-time adaptive processing (STAP) radar against the uncertainty of target parameters. Finally, an FPP-SCA algorithm is employed to solve the maximin nonconvex joint design problem. Simulation results demonstrate the effectiveness of the proposed method in terms of better output SCNR, lower computational load, and more robustness against the errors of target parameters.

Polarization space time adaptive processing based on maximum likelihood polarization vector estimation

In airborne radar, signal to clutter plus noise ratio (SCNR) can be improved by utilizing polarization space time adaptive processing (PSTAP). The optimal output SCNR is obtained when the expected polarization-space-time (PST) vector matches the true value. The problem of polarization parameters estimation limits the potential application of PSTAP. The inaccuracy of polarization parameters estimation unevitably causes SCNR loss. In this paper, the effect of expected polarization vector on output SCNR is analyzed on the basis of PSTAP theory. In order to reduce SCNR loss introduced by polarization mismatch, an engineering PSTAP based on maximum likelihood method is proposed. Experimental data show that this method can accurately estimate the polarization parameters, improving SCNR about 2~3dB compared with the conventional STAP.

Sub-CPI STAP based clutter suppression and target refocusing with airborne radar system

Under the interference of airborne radar clutter, it is a challenge for fast maneuvering target detection, because of the nonstationary echo signal involved within the long coherent processing interval (CPI). To tackle this issue, a robust clutter suppression and small maneuvering target detection method is proposed in airborne radar system, which is referred to as the sub-CPI space-time adaptive processing (STAP) based refocusing. For robust clutter suppression, the whole CPI is divided into a series of equal-length and overlapped sub-CPIs with the sliding window technique. In each sub-CPI, the STAP algorithm is adopted for robust clutter suppression, in which the consistent covariance matrix estimation is derived to guarantee the linear phase response within the whole CPI. However, as endo-clutter weak target is considered, the signal-to-clutter-plus-noise ratio (SCNR) after sub-CPI STAP still cannot meet the performance requirement. Thus, the target refocusing is applied for further SCNR improvement based on the semi-searching mechanism. However, the range walking (RW) and Doppler frequency migration problems defocus the energy of fast maneuvering target over range and Doppler frequency dimensions, respectively. To circumvent such problems, Keystone transform is utilized to correct the RW, followed by the Lv's distribution to achieve target refocusing. Numerical simulations and measured results with sum and difference channels validate the effectiveness of the proposed method.

An OFDM Chirp Waveform Design Method Based on Multiple Groups of Subchirp Durations Optimization for Clutter Suppression

Orthogonal frequency division multiplexing (OFDM) chirp waveform is considered a good choice in the waveform design for clutter suppression, which is due to its excellent characteristics such as spectral containment, phase diversity, great dynamic spectral allocation and high degree of freedom. Considering the purpose of clutter suppression, an OFDM chirp waveform design method based on multiple groups of subchirp durations optimization is proposed to improve the output signal-to-clutter-plus-noise ratio (SCNR) in this paper. The output SCNR is closely related to the waveform spectrum, so the waveforms’ energy spectral density functions are analyzed first to build the relation between the waveform parameters and spectra. Then, the multiple groups of subchirp durations optimization based on maximum SCNR is proposed and solved by an optimization method based on the sequential quadratic programming. Finally, the proposed method is verified and the optimized waveform is compared with the general waveform and the waveform with optimized single group of subchirp durations. The results show that the waveform optimized by the proposed method has higher output SCNR and the SCNR increment compared with the general waveform increases with the number of subcarriers. Besides, the high sidelobes of the general waveform are also greatly reduced.

Clutter Suppression for Wideband Radar STAP

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” 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.

Multi-Resolution STAP for Enhanced Ultra-Low-Altitude Target Detection

In this paper, an ultra-low-altitude target (ULAT) detection approach, referred to as the multi-resolution space-time adaptive processing (STAP), is proposed to enhance the target detection performance in a missile-borne radar system. In this respect, the whole base band is divided into a series of equal-width and center-frequency-diverse sub-bands with the frequency diversity technique, which enhances the multipath-target coupled (MTC) effect with the decreased range resolution. Hence, it is feasible to exploit the multipath signal power to improve the output signal-to-clutter-plus-noise ratio (SCNR) performance of sub-band STAP. In this regard, the mechanism of the MTC effect is analyzed numerically for the efficient sub-band STAP. However, such SCNR improvement is achieved at the cost of target tracking performance loss. Hence, the full-band STAP is further applied for multipath-target separation based on the target range-Doppler locations detected by the joint multiple sub-bands ΣΔ-STAP, which also alleviates the dynamic target attenuation and the corresponding target Doppler history corruption within the long coherent processing interval (CPI). On this basis, the SCNR performance is further improved by applying coherent accumulation among sub-CPIs, in which the clutter suppression performance degradation and coherent accumulation loss of STAP are alleviated within the sub-CPIs. Numerical and measured results corroborate the effectiveness of ULAT detection with the considered multi-resolution STAP.

Angle-Doppler Channel Selection Method for Reduced-Dimension STAP Based on Sequential Convex Programming

This letter considers the dimension reduction for the space-time adaptive processing (STAP). An angle-Doppler channel selection strategy to maximize the lower bound of the output signal-to-clutter-plus-noise ratio (SCNR) is proposed. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form based on the majorization-minimization (MM) algorithm and solve it in an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance, especially when the number of samples are limited.

Sea clutter suppression algorithm in spatial domain based on singular value decomposition

In order to detect the targets in the first-order sea clutter spectrum of high frequency surface wave radar (HFSWR), a method based on singular value decomposition (SVD) is proposed to suppress sea clutter in spatial domain. The sea clutter suppression method is based on the strong correlation of sea clutter, thus we take the adjacent range bin as the reference. Then the sea clutter subspace and noise subspace in the spatial domain is estimated by singular value decomposition of its array covariance matrix. Finally, we subtract its projection in the sea clutter subspace from the array signal with the orthogonality of subspace, and achieves the purpose of suppressing sea clutter in the spatial domain. Compared with the existing methods of sea clutter suppression in spatial domain, there is no need to know the azimuth of sea clutter in advance. The subspace is estimated by singular value decomposition of its array covariance matrix, which makes it easier and more accurate to estimate the sea clutter subspace and noise subspace. The result shows that the method improves the signal to clutter plus noise ratio (SCNR), which is benefit to target detection.

Joint design of transmit beamspace and reduced‐dimension receiver filter for MIMO radar STAP

In this article, the problem of the transmit beamspace (TB) matrix design in multiple-input multiple-output (MIMO) radar is considered. To reduce the computational burden and to focus the energy within optimal spatial sector, a joint design method of the TB matrix and a reduced-dimension space-time adaptive processing (STAP) filter is proposed based on the maximum signal-to-clutter-plus-noise ratio (SCNR). Firstly, the signal model of the reduced-dimension STAP for TB-based MIMO radar is established. Then, using the maximum SCNR as the design metric, the joint design model of the TB matrix and the reduced-dimension STAP filter is presented. Furthermore, the cyclic optimisation method is exploited to deal with the non-convex joint design problem with the energy constraint. Finally, simulation experiments demonstrate the joint design method of the TB matrix and the STAP filter not only has better output SCNR performance but also owns lower computational burden when compared with the other existing methods.

Adaptive Waveform Design for Automotive Joint Radar-Communication Systems

Unified waveform design for automotive joint radar-communications (JRC) leverages the scarce spectrum efficiently and has become a key topic for investigation of late. Designing such a waveform necessitates meeting the requirements of both systems, thereby making it a challenging task. The contribution of this paper is to formulate the JRC design problem into an optimization problem and propose an algorithm to maximize the signal-to-clutter-plus-noise-ratio (SCNR) of radar system and signal-to-noise-ratio (SNR) at communicating vehicle, simultaneously. Central to this are the exploitation of the communication link to acquire environment/ channel information and enhance radar tasks, flexibility to impart trade-off between the two systems during design as well the formulation of the optimization problem to include sidelobe constraints and yield solutions robust to Doppler shifts. The designed waveforms exhibit enhanced radar performance in terms of probability of detection and communication performance in terms of bit error rate (BER), while taking into account the trade-off between two systems. The numerical simulations corroborate the claim of optimized performance with environment/ channel information, ease of effecting trade-off and the use of design flexibility.

A STAP method based on atomic norm minimization for transmit beamspace-based airborne MIMO radar

The output signal-to-clutter-plus-noise ratio (SCNR) of space-time adaptive processing (STAP) decreases due to the dispersion of the transmit energy for traditional airborne multiple-input-multiple-output (MIMO) radar. Moreover the sufficient training samples cannot be provided to estimate the clutter covariance matrix (CCM) in the non-stationary environment. To solve these problems, a novel STAP method based on the atomic norm minimization (ANM) for transmit beamspace-based (TB-based) airborne MIMO radar is proposed. Firstly, the signal model of TB-based MIMO-STAP is established, then the optimizing principle based on the ANM is presented to design TB matrix used to focus the transmit energy in a certain spatial sector. Moreover, the beampattern corresponding to the TB matrix is close to the desired beampattern in different working modes. Meanwhile, to further reduce the number of the training samples, an accurate CCM estimation with the low-rank property is yielded by applying the ANM theory into the TB-based MIMO-STAP. Compared with the conventional MIMO-STAP with the orthogonal waveforms, simulation results show that the output SCNR of TB-based MIMO-STAP is improved. Moreover, since the CCM of the proposed method is estimated in continuous domain, the off-grid problem can be avoided. Thus, the clutter suppression performance is superior over the conventional sparse representation based STAP (SR-STAP). Meanwhile, simulation results show that the performance of the proposed method is close to that of the statistical classical MIMO-STAP and outperforms that of SR-STAP. Furthermore, it can be shown from the simulation results that the clutter suppression performance is still robust when the low-rank property of the CCM is destroyed.

FDA radar with doppler-spreading consideration: Mainlobe clutter suppression for blind-doppler target detection

This paper proposes a mainlobe clutter suppression approach for frequency diverse array (FDA) radar blind-Doppler target detection, by exploiting the Doppler-spreading (DS) effect. As an emerging array processing technique, FDA differs from conventional phased-array in that it employs a frequency increment across the array elements. When a large frequency increment is used, the FDA radar echo signal from a moving target will be spectrally spread in Doppler domain. Inspired by this phenomenon, we establish a joint range-angle-Doppler processing model for FDA radar with DS consideration. Using resolution capability analysis, we show that that this DS effect provides potentials in resolving Doppler ambiguity and meanwhile suppressing mainlobe clutters. As an application example, the proposed FDA radar model with DS consideration is used for blind-Doppler target detection in mainlobe clutters. Analytical expressions for the detection probability and signal-to-clutter-plus-noise ratio (SCNR) are derived for the proposed FDA-based target detection. Numerical results show that the proposed approach outperforms the counterparts for the FDA radar without DS consideration and conventional radars.

A Single-Dataset-Based Pre-Processing Joint Domain Localized Algorithm for Clutter-Suppression in Shipborne High-Frequency Surface-Wave Radar.

Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data.