This paper addresses target detection in passive multiple-input multiple-output (MIMO) radar networks comprised of non-cooperative transmitters and multichannel receivers. A generalized likelihood ratio test is derived, and approximate test statistic distributions are presented for both hypotheses under common scenario conditions. Analysis and simulation results show that this detector outperforms other passive MIMO radar detectors because it exploits more correlations within the measurement data. This detector is also compared against related detectors for active MIMO radar and passive source localization sensor networks. These comparisons reveal that passive MIMO radar detection performance varies between active MIMO radar and passive source localization detection performance as a function of direct-path signal quality. Therefore, passive MIMO radar unifies active MIMO radar and passive source localization sensor networks in a common theoretical framework.

Detection in Passive MIMO Radar Networks

In this paper, we consider the problem of joint delay-Doppler estimation of moving targets in a passive radar that makes use of orthogonal frequency-division multiplexing communication signals. A compressed sensing algorithm is proposed to achieve supper resolution and better accuracy, using both the atomic norm and the $\ell _1$-norm. The atomic norm is used to manifest the signal sparsity in the continuous domain. Unlike previous works that assume the demodulation to be error free, we explicitly introduce the demodulation error signal whose sparsity is imposed by the $\ell _1$-norm. On this basis, the delays and Doppler frequencies are estimated by solving a semidefinite program (SDP) which is convex. We also develop an iterative method for solving this SDP via the alternating direction method of multipliers where each iteration involves closed-form computation. Simulation results are presented to illustrate the high performance of the proposed algorithm.

Super-Resolution Delay-Doppler Estimation for OFDM Passive Radar

In the new era of integrated computing with intelligent devices and system, moving aerial targets can be tracked flexibly. The estimation performance of traditional matched filter-based methods would deteriorate dramatically for multiple targets tracking, since the weak target is masked by the strong target or the strong sidelobes. In order to solve the problems mentioned above, this paper aims at developing a joint range-Doppler-angle estimation solution for an intelligent tracking system with a commercial frequency modulation radio station (noncooperative illuminator of opportunity) and a uniform linear array. First, a gridless sparse method is proposed for simultaneous angle-range-Doppler estimation with atomic norm minimization. Based on the integrated computing, multiple workstations or servers of the data process center in the intelligent tracking system can cooperate with each other to accelerate the data process. Then a suboptimal method, which estimates three parameters in a sequential way, is proposed based on grid sparse method. The range-Doppler of each target is iteratively estimated by exploiting the joint sparsity in multiple surveillance antennas. A simple beamforming method is used to estimate the angles in turn by exploiting the angle information in the joint sparse coefficients. Simulation result and real test show that the proposed solution can effectively detect weak targets in an iterative manner.

Joint Range-Doppler-Angle Estimation for Intelligent Tracking of Moving Aerial Targets

This paper presents a description of recent research and development in HF passive bistatic radar (HFPBR) based on DRM digital AM broadcasting at Wuhan University, China First, preliminary evaluation of its detection performance with special focus on the hybrid sky-surface wave propagation mode is introduced Then, DRM broadcasting signal analysis as a radar waveform and associated signal processing techniques are described, consisting of ambiguity function analysis, reference signal extraction, multipath clutter rejection, and target localization Finally, the experimental system and experimental data analysis are provided Initial results from field experiments show that DRM-based HFPBR with hybrid sky-surface wave is a promising system for wide area moving target detection and ocean remote sensing

An Experimental Study of HF Passive Bistatic Radar Via Hybrid Sky-Surface Wave Mode

This paper considers the problem of passive detection with a multistatic radar system involving a noncooperative illuminator of opportunity (IO) and multiple receive platforms. An unknown source signal is transmitted by the IO, which illuminates a target of interest. These receive platforms are geographically dispersed, and collect independent target echoes due to the illumination by the same IO. We consider a generalized canonical correlation (GCC) detector for passive detection which requires the knowledge of the noise power. We derive closed-form expressions for the probabilities of false alarm and detection of this detector. For the case where the noise power is unknown, we propose a generalized likelihood ratio test (GLRT) detector to deal with the passive detection problem. Moreover, a closed-form expression for the probability of false alarm of this GLRT detector is given, which shows that the proposed GLRT detector exhibits a constant false alarm rate property with respect to the noise power. Numerical simulations demonstrate that the proposed GLRT detector generally outperforms the generalized coherence detector, a previous popular passive detector that neither requires the knowledge of the noise power. In addition, the GLRT also outperforms the GCC detector when the latter has an uncertainty in its knowledge of the noise power.

Two Target Detection Algorithms for Passive Multistatic Radar

The signal model of digital television terrestrial broadcasting (DTTB) is introduced and its ambiguity function is analysed here. In order to suppress the direct-path (and multipath) interference generated by single frequency network in DTTB-based passive radar, the corresponding spatial filtering methods are designed according to the feature of each channel. In the echo channel, the interference is suppressed by discarding the signal-subspace. The noise-subspace can be estimated from the power of the covariance matrix to avoid the eigen-decomposition and to eliminate the need to obtain the corresponding dimension. In the reference channel, the transmitting signal of a certain transmitter is regarded as the desired signal. The desired signal will be cancelled by conventional spatial filtering algorithms for the high signal-to-noise ratio case, which will also result in a distorted pattern and high side-lobes. To solve this problem, an improved general side-lobe canceller structure is proposed to realise the adaptive beamformer. To improve the robustness of the proposed methods and suppress the distributed clutter, the ‘broad null’ algorithm is combined with the beamformer. The simulation results show that the proposed methods are effective to suppress the interference in DTTB-based passive radar.

H.Z. Wu

Papers

Direct-path suppression by spatial filtering in digital television terrestrial broadcasting-based passive radar