Target tracking in passive multi-static radar (PMSR) with bistatic range and Doppler frequency measurements from multiple transmit-receive pairs is gaining increasing interest. For the data association problem in this scenario, the parallel architecture of a multi-sensor joint probabilistic data association (P-MSJPDA) filter has been significantly investigated. As an alternative architecture, the sequential MSJPDA (S-MSJPDA) is rarely discussed in PMSR. In this paper, we evaluate the behaviors of S-MSJPDA in PMSR target tracking with bistatic range and Doppler frequency measurements. A comprehensive comparison between the S-MSJPDA and the P-MSJPDA in PMSR is provided. It can be found from the analysis that S-MSJPDA outperforms its parallel counterpart in terms of computational efficiency, given an acceptable degradation in position accuracy. The S-MSJPDA is further applied to an experimental passive multi-static radar for aircrafts tracking. The real data results obtained are rather close to the true trajectories of the targets. This demonstrates that the S-MSJPDA has great potentials in PMSR target tracking. INDEX TERMS Passive multi-static radar, target tracking, multi-sensor joint probabilistic data association.
This letter considers the interference‐to‐noise ratio (INR) estimation problem in long‐term evolution (LTE)‐based passive bistatic radar. The INR is of great importance in the evaluation of interference level, the prediction of the target detection capability and the target parameter estimation. Traditional passive radar uses the cross‐correlation function between the reference and surveillance signals to estimate the INR. This is not applicable to LTE‐based passive radar due to the uncorrelation between the interference and reference signals in LTE‐based passive radar. In this letter, we propose a cyclic auto‐correlation method to estimate the INR. Simulation results show the effectiveness of the proposed method.
The Inmarsat Broadband Global Area Network (BGAN) L-band downlink signal from a radar viewpoint is studied. The signal is modelled and its ambiguity function (AF) is analysed and the AF is compared with that of the real recorded signal. It is shown that the AF of a single Inmarsat channel has an interference floor of −40 dB for 80 ms integration time. The AF of the Inmarsat BGAN signal is improved by combining multiple frequency channels with channel overlap.
Most traditional direction of arrival (DOA) estimation methods in passive radar are based on the parametric model of the antenna array manifold, and lack the adaption to the array errors. The data-driven machine learning-based methods have great array error adaption capability. However, most existing machine learning-based methods cannot be applied directly to the passive radar DOA estimation, because the array covariance matrix that they use as the input is not easy to estimate with adequate accuracy in passive radar owing to the poor target signal to clutter plus noise ratio (SCNR). A deep learning-based method for DOA estimation in passive radar is proposed here. Clutter cancelation and range-Doppler cross-correlation (RDCC) is performed to increase the target SCNR. The RDCC result is taken as the input of the deep learning method, and the amplitude and phase uncertainties of the RDCC result are treated. A two-stage deep neural network (DNN) is designed. The first stage determines the spatial sub-region of the target, and the second stage gets the refined DOA estimation. Simulations show that the proposed twostage DNN well outperforms the traditional passive radar DOA estimation method and the multi-layer perceptron network. Real experiments verify the superiority of the proposed method.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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