The Experts below are selected from a list of 37596 Experts worldwide ranked by ideXlab platform
P P Vaidyanathan - One of the best experts on this subject based on the ideXlab platform.
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coprime arrays and samplers for space time Adaptive Processing
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Chunlin Liu, P P VaidyanathanAbstract:This paper extends the use of coprime arrays and samplers for the case of moving sources. Space-Time Adaptive Processing (STAP) plays an important role in estimating direction-of-arrivals (DOAs) and radial velocities of emitting sources. However, the detection performance is fundamentally limited by the array geometry and the temporal samplers at each sensor. Coprime arrays and coprime samplers offer an enhanced degree of freedom of O(MN) using only O(M + N) physical sensors or samples. In this paper, we propose coprime joint angle-Doppler estimation (coprime JADE), which incorporates both coprime arrays and coprime samplers with the STAP framework. Nonuniform time samples at different sensors can be used to generate a sampled autocorrelation matrix, from which we compute a spatial smoothed matrix. It will be proved that spatial smoothed matrices can be used in the MUSIC algorithm for parameter estimation. With sufficient snapshots, coprime JADE distinguishes O(M 1 N 1 M 2 2 ) independent sources if it corresponds to coprime arrays and coprime samplers with coprime integers (M 1 ; 1 ) and (M 2 ;N 2 ), respectively. It is verified through simulations that coprime JADE resolves the angle-Doppler information better compared to other conventional algorithms.
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mimo radar space time Adaptive Processing using prolate spheroidal wave functions
IEEE Transactions on Signal Processing, 2008Co-Authors: Chunyang Chen, P P VaidyanathanAbstract:In the traditional transmitting beamforming radar system, the transmitting antennas send coherent waveforms which form a highly focused beam. In the multiple-input multiple-output (MIMO) radar system, the transmitter sends noncoherent (possibly orthogonal) broad (possibly omnidirectional) waveforms. These waveforms can be extracted at the receiver by a matched filterbank. The extracted signals can be used to obtain more diversity or to improve the spatial resolution for clutter. This paper focuses on Space-Time Adaptive Processing (STAP) for MIMO radar systems which improves the spatial resolution for clutter. With a slight modification, STAP methods developed originally for the single-input multiple-output (SIMO) radar (conventional radar) can also be used in MIMO radar. However, in the MIMO radar, the rank of the jammer-and-clutter subspace becomes very large, especially the jammer subspace. It affects both the complexity and the convergence of the STAP algorithm. In this paper, the clutter space and its rank in the MIMO radar are explored. By using the geometry of the problem rather than data, the clutter subspace can be represented using prolate spheroidal wave functions (PSWF). A new STAP algorithm is also proposed. It computes the clutter space using the PSWF and utilizes the block-diagonal property of the jammer covariance matrix. Because of fully utilizing the geometry and the structure of the covariance matrix, the method has very good SINR performance and low computational complexity.
Petre Stoica - One of the best experts on this subject based on the ideXlab platform.
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polyphase waveform design for mimo radar space time Adaptive Processing
IEEE Transactions on Signal Processing, 2020Co-Authors: Bo Tang, Jonathan Tuck, Petre StoicaAbstract:We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-input-multiple-output (MIMO) radar. Due to the constant-modulus and finite-alphabet constraint on the waveforms, the associated design problem is non-convex and in general NP-hard. To tackle this problem, we develop an efficient algorithm based on relaxation and cyclic optimization. Moreover, we exploit a reparameterization trick to avoid the significant computational burden and memory requirement brought about by relaxation. We prove that the objective values during the iterations are guaranteed to converge. Finally, we provide an effective randomization approach to obtain polyphase waveforms from the relaxed solution at convergence. Numerical examples show the effectiveness of the proposed algorithm for designing polyphase waveforms.
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knowledge aided space time Adaptive Processing
IEEE Transactions on Aerospace and Electronic Systems, 2011Co-Authors: Xumin Zhu, Petre StoicaAbstract:A fundamental issue in knowledge-aided Space-Time Adaptive Processing (KA-STAP) is to determine the degree of accuracy of the a~priori knowledge and the optimal emphasis that should be placed on it. In KA-STAP, the a priori knowledge consists usually of an initial guess of the clutter covariance matrix. This can be obtained either by previous radar probings or by a map-based study. We consider a linear combination of the a~priori clutter covariance matrix with the sample covariance matrix obtained from secondary data, and derive an optimal weighting factor on the a priori knowledge by a maximum likelihood (ML) approach. The performance of the ML approach for KA-STAP is evaluated based on numerically simulated data.
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on using a priori knowledge in space time Adaptive Processing
IEEE Transactions on Signal Processing, 2008Co-Authors: Petre Stoica, Xumin Zhu, J R GuerciAbstract:In Space-Time Adaptive Processing (STAP), the clutter covariance matrix is routinely estimated from secondary ldquotarget-freerdquo data. Because this type of data is, more often than not, rather scarce, the so-obtained estimates of the clutter covariance matrix are typically rather poor. In knowledge-aided (KA) STAP, an a priori guess of the clutter covariance matrix (e.g., derived from knowledge of the terrain probed by the radar) is available. In this note, we describe a computationally simple and fully automatic method for combining this prior guess with secondary data to obtain a theoretically optimal (in the mean-squared error sense) estimate of the clutter covariance matrix. The authors apply the proposed method to the KASSPER data set to illustrate the type of achievable performance.
Zhaocheng Yang - One of the best experts on this subject based on the ideXlab platform.
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sparsity based space time Adaptive Processing for airborne radar with coprime array and coprime pulse repetition interval
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Xiaoye Wang, Zhaocheng Yang, Jianjun HuangAbstract:In this paper, we present a sparsity-based Space-Time Adaptive Processing (STAP) algorithm with coprime array and coprime pulse repetition interval (PRI). The considered Space-Time coprime configuration can significantly save the cost. However, the direct STAP does not exploit the advantage of the large aperture brought by coprime configuration and the recently developed spatial-temporal smoothed-based STAP requires a large number of training snapshots. To solve these issues, we propose a sparsity-based STAP algorithm by using the spacial-temporal sparsity of clutter in virtual domain. Simulation results show that the proposed algorithm can obtain a much higher output signal-to-interference-plus-noise ratio and improve the convergence speed.
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space time Adaptive Processing for clutter suppression in coprime array and coprime pulse repetition interval airborne radar
International Symposium on Intelligent Signal Processing and Communication Systems, 2017Co-Authors: Xiaoye Wang, Zhaocheng Yang, Jianjun HuangAbstract:This paper develops two novel Space-Time Adaptive Processing (STAP) filters for clutter suppression in airborne radar with the coprime Space-Time sampling, which is realized by the coprime array and coprime pulse repetition interval (PRI). Different from the conventional STAP filters, the proposed STAP filters are derived by three steps. Firstly, a virtual Space-Time snapshot is constructed using the property of the coprime sampling. Secondly, an equivalent covariance matrix with enhanced degrees of freedom is computed by using spatial-temporal smoothing approach. Thirdly, two optimal STAP filters are derived based on the estimated covariance matrix. Simulations are conducted to validate the effectiveness of the proposed filters.
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space time Adaptive Processing by enforcing sparse constraint on beam doppler patterns
Electronics Letters, 2017Co-Authors: Zhaocheng YangAbstract:In this Letter, the author proposes a novel space–time Adaptive Processing (STAP) algorithm by enforcing sparse constraint on the beam-Doppler patterns for clutter mitigation when the number of training data is limited. By exploiting the sparsity of the beam-Doppler patterns of the STAP filter, the proposed algorithm formulates the filter design as a mixed l 2-norm and l 1-norm minimisation problem. Moreover, the proposed algorithm develops an Adaptive approach to update the regularisation parameter. Simulation results illustrate that the proposed algorithm outperforms the traditional STAP algorithms in a limited sample support.
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space time Adaptive Processing airborne radar with coprime pulse repetition interval
IEEE International Radar Conference, 2016Co-Authors: Zhaocheng Yang, Jingxiong Huang, Jianjun Huang, Li KangAbstract:In this paper, motivated by the success of coprime array in the direction-of-arrival (DOA) estimation, we introduce the idea of coprime pulse repetition interval (PRI) into the Space-Time Adaptive Processing (STAP) airborne radar. Through transmitting and receiving the pulses with coprime PRI, we can reduce the transmitting energy and improve the capabilities of electronic counter-countermeasures (ECCM). We use the lags between the receiving pulses to construct virtual pulses. By using the virtual pulses, we can obtain a new snapshot with a larger dimension than the real one. The constructed snapshots are exploited to estimate the clutter-plus-noise covariance matrix and then to form the STAP filter. Simulation results show that the proposed coprime PRI strategy STAP radar can achieve a good performance with reduced pulses.
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compressive space time Adaptive Processing airborne radar with random pulse repetition interval and random arrays
International Workshop on Compressed Sensing Theory and its Applications to Radar Sonar and Remote Sensing, 2016Co-Authors: Zhaocheng Yang, Guihua Quan, Jianjun HuangAbstract:This paper proposes a compressive Space-Time Adaptive Processing (CSTAP) airborne radar by reducing the samples in one coherence Processing interval with random pulse repetition interval and random arrays, termed as the RPRI-RA radar. We firstly detail the compressive sampling in spatial and Doppler domain and develop a rule for the clutter rank estimation for the RPRI-RA radar. Then, CSTAP using the compressed samples without sparse reconstruction is proposed to mitigate the clutter. Additionally, from the point view of degrees of freedom, we perform a preliminary investigation about the configuration design for the target detection. Simulation results illustrate the effectiveness of above contents of the developed RPRI-RA radar.
Jianjun Huang - One of the best experts on this subject based on the ideXlab platform.
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sparsity based space time Adaptive Processing for airborne radar with coprime array and coprime pulse repetition interval
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Xiaoye Wang, Zhaocheng Yang, Jianjun HuangAbstract:In this paper, we present a sparsity-based Space-Time Adaptive Processing (STAP) algorithm with coprime array and coprime pulse repetition interval (PRI). The considered Space-Time coprime configuration can significantly save the cost. However, the direct STAP does not exploit the advantage of the large aperture brought by coprime configuration and the recently developed spatial-temporal smoothed-based STAP requires a large number of training snapshots. To solve these issues, we propose a sparsity-based STAP algorithm by using the spacial-temporal sparsity of clutter in virtual domain. Simulation results show that the proposed algorithm can obtain a much higher output signal-to-interference-plus-noise ratio and improve the convergence speed.
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space time Adaptive Processing for clutter suppression in coprime array and coprime pulse repetition interval airborne radar
International Symposium on Intelligent Signal Processing and Communication Systems, 2017Co-Authors: Xiaoye Wang, Zhaocheng Yang, Jianjun HuangAbstract:This paper develops two novel Space-Time Adaptive Processing (STAP) filters for clutter suppression in airborne radar with the coprime Space-Time sampling, which is realized by the coprime array and coprime pulse repetition interval (PRI). Different from the conventional STAP filters, the proposed STAP filters are derived by three steps. Firstly, a virtual Space-Time snapshot is constructed using the property of the coprime sampling. Secondly, an equivalent covariance matrix with enhanced degrees of freedom is computed by using spatial-temporal smoothing approach. Thirdly, two optimal STAP filters are derived based on the estimated covariance matrix. Simulations are conducted to validate the effectiveness of the proposed filters.
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space time Adaptive Processing airborne radar with coprime pulse repetition interval
IEEE International Radar Conference, 2016Co-Authors: Zhaocheng Yang, Jingxiong Huang, Jianjun Huang, Li KangAbstract:In this paper, motivated by the success of coprime array in the direction-of-arrival (DOA) estimation, we introduce the idea of coprime pulse repetition interval (PRI) into the Space-Time Adaptive Processing (STAP) airborne radar. Through transmitting and receiving the pulses with coprime PRI, we can reduce the transmitting energy and improve the capabilities of electronic counter-countermeasures (ECCM). We use the lags between the receiving pulses to construct virtual pulses. By using the virtual pulses, we can obtain a new snapshot with a larger dimension than the real one. The constructed snapshots are exploited to estimate the clutter-plus-noise covariance matrix and then to form the STAP filter. Simulation results show that the proposed coprime PRI strategy STAP radar can achieve a good performance with reduced pulses.
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compressive space time Adaptive Processing airborne radar with random pulse repetition interval and random arrays
International Workshop on Compressed Sensing Theory and its Applications to Radar Sonar and Remote Sensing, 2016Co-Authors: Zhaocheng Yang, Guihua Quan, Jianjun HuangAbstract:This paper proposes a compressive Space-Time Adaptive Processing (CSTAP) airborne radar by reducing the samples in one coherence Processing interval with random pulse repetition interval and random arrays, termed as the RPRI-RA radar. We firstly detail the compressive sampling in spatial and Doppler domain and develop a rule for the clutter rank estimation for the RPRI-RA radar. Then, CSTAP using the compressed samples without sparse reconstruction is proposed to mitigate the clutter. Additionally, from the point view of degrees of freedom, we perform a preliminary investigation about the configuration design for the target detection. Simulation results illustrate the effectiveness of above contents of the developed RPRI-RA radar.
Bo Tang - One of the best experts on this subject based on the ideXlab platform.
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polyphase waveform design for mimo radar space time Adaptive Processing
IEEE Transactions on Signal Processing, 2020Co-Authors: Bo Tang, Jonathan Tuck, Petre StoicaAbstract:We consider the design of polyphase waveforms for ground moving target detection with airborne multiple-input-multiple-output (MIMO) radar. Due to the constant-modulus and finite-alphabet constraint on the waveforms, the associated design problem is non-convex and in general NP-hard. To tackle this problem, we develop an efficient algorithm based on relaxation and cyclic optimization. Moreover, we exploit a reparameterization trick to avoid the significant computational burden and memory requirement brought about by relaxation. We prove that the objective values during the iterations are guaranteed to converge. Finally, we provide an effective randomization approach to obtain polyphase waveforms from the relaxed solution at convergence. Numerical examples show the effectiveness of the proposed algorithm for designing polyphase waveforms.
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non linear shrinkage based precision matrix estimation for space time Adaptive Processing
Iet Radar Sonar and Navigation, 2017Co-Authors: Dandan Zhang, Jun Tang, Bo TangAbstract:Space–time Adaptive Processing (STAP) is usually adopted in airborne and spaceborne radars for clutter suppression and slow moving target detection. However, for traditional sample covariance matrix inversion algorithm, there are usually not enough independent and identically distributed secondary samples to achieve a satisfactory performance. The diagonal loading methods are usually utilised to solve this finite sample problem. Nevertheless, the performance of diagonal loading STAP can be further improved by directly estimating the precision matrix and non-linearly shrinking the eigenvalues of the samples covariance matrix. This study proposes a non-linear shrinkage-based precision matrix estimation algorithm for STAP. Also, a noise power estimation method is provided so that the proposed algorithm still works well even if the noise power is not known previously. The proposed algorithm has much better performance than the diagonal loading methods whether the sample size is smaller or larger than the dimension of the covariance matrix. Besides, in the condition that the clutter-to-noise ratio is large, which is often encountered in practice, the proposed algorithm also shows a comparatively good performance. Simulations are performed to validate the proposed algorithm.
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joint design of transmit waveforms and receive filters for mimo radar space time Adaptive Processing
IEEE Transactions on Signal Processing, 2016Co-Authors: Bo Tang, Jun TangAbstract:This paper considers the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems. The aim is to maximize the output signal-to-interference-plus-noise ratio (SINR) such that we can achieve enhanced detection performance for a slow-moving target that might be obscured by clutter and jamming. We devise a cyclic algorithm to tackle the non-convex joint design problem. Considering practical implementation issues, we extend the algorithm to deal with constrained designs (i.e., the design of waveforms under a constant-envelope constraint and a similarity constraint, respectively). The convergence of all the devised algorithms is guaranteed. We provide several numerical examples to demonstrate the effectiveness of the proposed algorithms.
