Bo Jiu

Superresolution ISAR Imaging Based on Sparse Bayesian Learning

Recently, compressive sensing (CS) has been successfully used in inverse synthetic aperture radar (ISAR) imaging. Since the exact sparse reconstruction, i.e., l0-norm constraint, is NP hard, l1-norm relaxation is widely used at the cost of performance degradation in the sparseness of the solution. The performance of existing CS-based ISAR imaging algorithms is sensitive to the regularized factor, which should be adjusted manually. This makes the existing algorithms inconvenient to be used in practice. It is well known that sparse Bayesian learning (SBL) acts as an effective tool in regression and classification, which is closely related to the CS. Furthermore, all the necessary parameters can be estimated using an efficient evidence maximization procedure in SBL, which retains a preferable property of the l0-norm diversity measure and can give more sparse solution. Motivated by that, a fully automated ISAR imaging algorithm based on SBL is proposed in this paper. Experimental results based on simulated and measured data show that the proposed algorithm keeps a better balance between the computation load and the sparsity of the reconstruction signal than the existing algorithms.

Simultaneous Multibeam Resource Allocation Scheme for Multiple Target Tracking

A colocated multiple-input multiple-output (MIMO) radar system has the ability to address multiple beam information. However, the simultaneous multibeam working mode has two finite working resources: the number of beams and the total transmit power of the multiple beams. In this scenario, a resource allocation strategy for the multibeam working mode with the task of tracking multiple targets is developed in this paper. The basis of our technique is to adjust the number of beams and their directions and the transmit power of each beam through feedback, with the purpose of improving the worst tracking performance among the multiple targets. The Bayesian Cramér-Rao lower bound (BCRLB) provides us with a lower bound on the estimated mean square error (MSE) of the target state. Hence, it is derived and utilized as an optimization criterion for the resource allocation scheme. We prove that the resulting resource optimization problem is nonconvex but can be reformulated as a set of convex problems. Therefore, optimal solutions can be obtained easily, which greatly aids real-time resource management. Numerical results show that the worst case tracking accuracy can be efficiently improved by the proposed simultaneous multibeam resource allocation (SMRA) algorithm.

Power Allocation Algorithm for Target Tracking in Unmodulated Continuous Wave Radar Network

Unmodulated continuous wave (UCW) radar has been shown to have many unique features. With the recent development, UCW radar network has become an attractive platform for target tracking. In practice, to achieve better tracking performance, UCW radars are supposed to maximize their transmitted power, which may be in contradiction with the limited energy resources of themselves. Therefore, a performance-driven power allocation algorithm for Doppler-only target tracking in a UCW radar network is proposed. This algorithm can be viewed as a reaction of the cognitive transmitter to the environment perceived by the receiver to minimize the target state estimation mean square error with a given total power budget. The Bayesian Cramér-Rao lower bound gives a measure of the achievable optimum performance for target tracking and, importantly, it can be calculated predictively. Therefore, it is derived and utilized as an optimization criterion for the power allocation algorithm. The resulting optimization problem is proved to be convex, and hence, can be solved by gradient projection method. Numerical results show that the target tracking accuracy can be efficiently improved by the proposed algorithm.

Prior Knowledge-Based Simultaneous Multibeam Power Allocation Algorithm for Cognitive Multiple Targets Tracking in Clutter

In this paper, a power allocation scheme for tracking multiple targets, with radar measurements either target generated or false alarms, is developed for colocated multiple-input multiple-output (MIMO) radar system. Such a system adopts a multibeam concept, in which multiple simultaneous transmit beams are synthesized by different probing signals from various colocated transmitters. To ensure that the limited power resource can be exploited effectively, we adjust the transmit power of each beam according to the prior knowledge predicted from the tracking recursion cycle. Specifically, the whole algorithm can be viewed as a reaction of the cognitive transmitters to the environment, in order to improve the worst case tracking performance of the multiple targets. By incorporating an information reduction factor (IRF), the Bayesian Cramér-Rao lower bound (BCRLB) gives a measure of the best achievable performance for target tracking in clutter. Hence, it is derived and utilized as an optimization criterion for the simultaneous multibeam power allocation algorithm. The optimization problem is nonconvex and is solved by the modified gradient projection (MGP) method in this paper. Simulation results show that the proposed algorithm significantly outperforms equal power allocation, in terms of the worst case tracking root mean-square error (RMSE).

Minimax robust transmission waveform and receiving filter design for extended target detection with imprecise prior knowledge

It is well known that optimal extended target detection for wideband radar in the presence of colored Gaussian noise and signal-dependent interference can be implemented, based on the prior information of target impulse response, by transmit-receiver design via maximizing the output signal-to-interference plus noise ratio (SINR). However, the prior knowledge of the target is usually imprecise. The target impulse response is very sensitive to target-radar orientation, and the initial phase of target echo is a function of target-radar distance, namely, the exact target impulse response cannot be obtained in transmission waveform design. Additionally, the transmission waveform is desired to be of constant modulus for power efficiency. In this paper, we propose a robust method to jointly design the transmission waveform with constant modulus constraint and the receiving filters. The cost function is established by maximizing the worst-case output SINR and an iterative procedure is presented based on modified sequential quadratic programming. Numerical results show that the proposed method can increase the worst-case output SINR significantly.

Knowledge-Based Spatial-Temporal Hierarchical MIMO Radar Waveform Design Method for Target Detection in Heterogeneous Clutter Zone

Knowledge-based MIMO radar waveform design for target detection in heterogeneous clutter zone is addressed in this paper. In order to improve the detection probability efficiently, a new optimization cost function is developed via minimizing the output clutter peak level and peak sidelobe level of the correlation function on the premise of maintaining the output target signal energy. With constant modulus constraint of transmit waveform, the new cost function is an NP-hard problem. To tackle this problem, a spatial-temporal hierarchical optimization approach is proposed that can decompose the original problem to two hierarchical subproblems approximately. On the foundation of convex programming and cyclic algorithm, the first subproblem, i.e., knowledge-based transmit beampattern design, can be solved effectively. Based on CVX programming and the bi-iterative method, the joint mainlobe synthesized signal and mismatched receiving filter optimization method is proposed to solve the second subproblem. Numerical results show the efficiency of the proposed method.

Optimal mismatched filter bank design for MIMO radar via convex optimization

In this paper, a mismatched filter bank is designed for suppressing the autocorrelation peak sidelobe level (PSL) and the peak cross-correlation level (PCCL) of an orthogonal polyphase sequence set applied in a multiple-input multiple-output (MIMO) radar system. The mismatched filter bank is obtained by minimizing a weighted maximum of the PSL and PCCL on the basis of the convex optimization. Compared with the iteratively reweighted least squares (IRLS) method, the proposed convex method can get the optimal mismatched filter bank with the minimum PSL and PCCL, and can also control the system signal-to-noise ratio loss (SNRL). Numerical examples show that the optimal mismatched filter bank at the cost of a slight SNRL can achieve a good improvement of the PSL and a moderate improvement of the PCCL, if the filter length P and the weighting factor w between the PSL and PCCL are appropriately chosen.

A Novel ISAR Imaging Algorithm for Micromotion Targets Based on Multiple Sparse Bayesian Learning

A novel inverse synthetic aperture radar (ISAR) imaging algorithm for micromotion targets based on multiple sparse Bayesian learning (MSBL) is proposed. First, the signal of the main body is reconstructed by the MSBL method based on the property of its common profile. Subsequently, the signal of rotating parts can be obtained by removing the main body signal from the original signal. Finally, a clear ISAR image of the main body and the micromotion parameter of the rotating parts can be obtained. Numerical results based on simulated and measured data show that the proposed algorithm can not only acquire a clear ISAR image of the main body but also extract the micromotion parameter of the rotating parts effectively.

Wideband cognitive radar waveform optimization for joint target radar signature estimation and target detection

As the target radar signature (TRS) is target-radar orientation sensitive and time varying in wideband cognitive radar (WCR), the TRS information ofWCR should be recursively updated by the receiver on the fly. Therefore, in WCR waveform design for target detection, TRS estimation ability should also be considered to provide the prior knowledge for the optimization of the next waveform. To address this problem, a WCR waveform design method is proposed for target detection by maximizing the average signal to clutter plus noise ratio of the received echo on the premise of ensuring the TRS estimation precision. By constraining the estimation performance, a convex cost function is established and the optimal solution can be obtained by the existing convex programming algorithm. Furthermore, for the convenience of waveform optimization in real time, a fast hierarchical scheme is also proposed with comparative performance. Numerical results show that, the proposed methods are able to improve the target detection performance under given estimation performance constraint.

Joint ISAR Imaging and Cross-Range Scaling Method Based on Compressive Sensing With Adaptive Dictionary

Compressive sensing (CS) is successfully applied in inverse synthetic aperture radar (ISAR) imaging. But, as target rotation rate is not concerned in the CS-based imaging methods, the obtained image cannot be scaled in the cross-range dimension. Consequently, difficulties arise in extracting the target geometrical information from the CS ISAR image. But, target geometrical size is an important parameter in automatic radar target recognition. To remedy this problem, a joint ISAR imaging and cross-range scaling method is proposed. In the proposed method, an adaptive parametric dictionary, comprising chirp rate parameter, is used to represent the observed data. By minimizing the reconstruction error, sparsity-constrained optimization, combined with the chirp-rate parameter and target reflective coefficient, is established. To find a solution to the nonlinear and nonconvex optimization problem, an iterative procedure is developed. Finally, with the help of the chirp-rate, target rotation rate can be estimated by the least square method, and the ISAR image can be scaled in cross-range. Experimental results show that the proposed method can fit the observed data better than the method using a fixed Fourier dictionary. Besides, cross-range scaled ISAR images can be obtained with limited pulses.