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Some new problems in distributed inverse synthetic aperture radar (ISAR) fusion imaging may be caused by constant frequency errors (CFE) due to the imperfect match of two independent oscillators. In this study, the effects of the CFE on distributed ISAR coherent fusion imaging are first analysed, which show that the CFE will produce false images (incorrect scatterer number and position) in distributed ISAR fusion imaging. To eliminate the effects of the CFE, a novel CFE calibration scheme is devised by exploiting the echo signal phase relationship between different sensors and making some reasonable approximations. The echo signal of the active radar (transmit and receive) is used as a reference signal. After some derivation, the phase relationship between the reference signal and other signals with CFE can be derived. Based on the derived phase relationship, the CFE of each radar will be estimated by multiple signal classification algorithm. Then, the estimated CFE value is further used to calibrate the CFE of each radar before signal coherent fusion. As a result, the false image is eliminated. Simulation results prove the authors theoretical analysis and the effectiveness of their method.

The linear time-varying frequency errors (LTFE) caused by the mismatch of transmitter and receiver oscillators can defocus the imaging result of distributed inverse synthetic aperture radar (ISAR) seriously. The LTFE calibration method based on the entropy minimization principle is sensitive to signal-to-noise ratio (SNR), and its performance is degraded significantly under low SNR conditions. In addition, this method uses enumeration algorithm to solve the optimization problem, which has a heavy computation burden. Therefore, a robust calibration method based on the contrast maximization principle is proposed. Compared with image entropy, image contrast has better anti-noise ability because it has better sensitivity property, namely, the change of image contrast is sharper than the change of image entropy. In the proposed method, the estimation of frequency error coefficient is modelled as an unconstrained optimization problem with image contrast as cost function, and the particle swarm optimization (PSO) algorithm is used to search the global optimal solution. Then, the LTFE can be calibrated by the estimated frequency error coefficient. The proposed method has better robustness, which can work well under low SNR conditions. Besides, it has higher computational efficiency. Simulations are carried out to verify the effectiveness and robustness of the proposed method.

Radar observations from different angles are often discontinuous in multistatic inverse synthetic aperture radar (ISAR) imaging. Based on Fourier transform, such as Polar Format Algorithm and Range Doppler Algorithm, the discontinuity of the angle will make the performance of traditional ISAR imaging algorithm worse. The sidelobe of the image will rise and the mainlobe may split. Generally, it is necessary to pre-process the gapped data and then the traditional ISAR imaging algorithm is used for imaging. The most commonly used pre-processing method is to interpolate the gap. However, the performance of this method is not satisfied, especially when the gap is large. The reason of sidelobe rising and mainlobe splitting is first analysed. Then, a sidelobe reduction method based on compressive sensing (CS) is proposed. This method establishes a relationship between the complete data and the gapped data, and the complete data can be solved from the gapped data by CS method. After that, the complete data will be used for imaging by utilising traditional ISAR imaging algorithm and the high sidelobe will be reduced effectively. The effectiveness of the proposed method is verified by the analysis and the simulation results.

In this paper, a robust target localization method based on iterative reweight least squares (IRWLS) is presented to against outliers in distributed MIMO radars. Unlike conventional weight least squares model which derived from the maximum likelihood estimator (MLE), the proposed robust global cost function is established based on least absolute model. Since the least absolute is nontrivial to solve directly, we convert it into a reweight least squares problem. After that, we linearize the relation between target location and bistatic range (BR) without introducing any nuisance parameters, which is different from existing linearization methods. The solution of proposed method can be obtained efficiently via iteration with closed-form expression. Simulation results validate the robust performance of proposed method under low SNR condition and outliers.

The inevitable frequency errors owing to the frequency mismatch of a transmitter and receiver oscillators could seriously deteriorate the imaging performance in distributed inverse synthetic aperture radar (ISAR) system. In this paper, for this issue, a novel method is proposed to calibrate the linear time-varying frequency errors (LTFE) between the transmitting node and the receiving node. The cost function is constructed based on the entropy minimization principle and the problem of LTFE calibration is transformed into cost function optimization. The frequency error coefficient, which minimizes the image entropy, is obtained by searching optimum solution in the solution space of cost function. Then, the original signal is calibrated by the frequency error coefficient. Finally, the effectiveness of the proposed method is demonstrated by simulation and real-data experiments.

In this paper, recognition issue of range deception jamming is considered for bistatic MIMO radar systems. Generally, two factors of the error caused by Taylor expansion and unconstrained statistical hypothesis test in traditional methods will lead to unfavorable recognition probabilities of range deception jamming and target. To handle these two problems, first, the novel functions are constructed with the bistatic range history, the transmit cone angle and the receive cone angle by utilizing the characteristic of bistatic MIMO radar. Second, a nonlinear transformation is introduced to overcome the error caused by Taylor expansion. Third, target and range deception jamming are recognized under the constrained F hypothesis test instead of χ 2 test. Compared with traditional methods, the proposed method exhibits superior recognition probability. At last, simulation results are given to illustrate the effectiveness of the proposed method.