For passive radar on moving platforms, the clutter suppression performance of the space-time adaptive processing (STAP) is degraded significantly. This paper reveals the underlying reason that the high sidelobes of strong clutter raise the noise eigenvalue power level of the space-time covariance matrix. To tackle this problem, we propose a Doppler separation-based clutter suppression method. The proposed method first separates the clutter at different Doppler frequencies, and then performs clutter suppression for each Doppler interval. The Doppler separation operation can mitigate the influence of clutter sidelobes effectively. In clutter Doppler separation, the construction criterions of Doppler extension subspace and time delay extension subspace are also established. Simulated and field experimental results demonstrate the effectiveness of the proposed method in suppressing strong clutter while avoiding significant loss of target signal to interference plus noise ratio (SINR).

In radar systems, target tracking errors are mainly from motion models and nonlinear measurements. When we evaluate a tracking algorithm, its tracking accuracy is the main criterion. To improve the tracking accuracy, in this paper we formulate the tracking problem into a regression model from measurements to target states. A tracking algorithm based on a modified deep feedforward neural network (MDFNN) is then proposed. In MDFNN, a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence, and the optimal measurement sequence size is analyzed. Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter (EKF), unscented Kalman filter (UKF), and recurrent neural network (RNN) based tracking methods under the considered scenarios.

In a passive radar, when the reference antenna is set to receive a direct-path signal, target signals will be inevitably mixed in the reference channel due to the antenna pattern. In this case, adaptive clutter cancellation in the temporal domain will affect the target steering vector due to the correlation between the target signal in the reference and surveillance channels, which results in a direction of arrival (DOA) estimation bias. The spatial-domain adaptive clutter rejection will also cause a similar influence. To solve this problem, in this article, we derive and analyze the effect of the adaptive clutter rejection on the target steering vector. Two kinds of DOA estimation methods are hence proposed to correct the estimation bias. The applicable boundaries of these two methods are also discussed. Simulations and real radar data show that the proposed methods can correct the DOA bias caused by adaptive clutter rejection, thereby improving the location accuracy in a passive radar.

In the frequency modulation (FM) radio-based passive radar system, the target signal mixed in the reference channel will have a negative impact on the target's own direction of arrival (DOA) estimation. To solve this problem, this letter proposes a robust DOA correction method. First, the digital beamforming (DBF) is used to obtain the reference signal. Second, the influence of the mixed target signal on the array steering vector is analyzed analytically. Finally, the DOA correction method is given. The effectiveness of the proposed method is validated using simulations and real-life data.

In the China digital radio (CDR)-based passive radar, when the range-Doppler (RD) map is generated by the classical matched filtering method, the target range profile has the mainlobe splitting phenomenon. To solve this problem, we first derive the reason for the target mainlobe splitting from the subcarrier domain. Then, we develop an effective-subcarrier-based complex fast sparse Bayesian learning (ES-CFSBL) algorithm to generate RD map. Finally, simulation and experimental results show that the developed algorithm can effectively reduce sidelobe level and make it easier to detect and track weak targets covered by strong target sidelobes. Besides, the proposed method can realize fractional delay estimation conveniently.

In this letter, we use the known pseudorandom noise (PN) signal in the digital television terrestrial multimedia broadcasting (DTMB) system as a new illuminator of opportunity for the passive radar. We propose a computationally efficient signal processing method for the PN-based passive radar (PNPR). The proposed method takes advantage of the distinctive structure of the PN signal and constructs a locally remapped PN signal. The circular cross-correlation between the remapped PN signal and the original PN signal is an ideal impulse function. Thus, the PNPR even does not need to do the clutter cancellation. The proposed method is validated via both the simulated and experimental data.

In scenarios involving persistent signal and clutter, where subspaces of clutter and signal of interest may be well characterized, the generalized likelihood ratio test (GLRT), also known as matched subspace detector, exploits only the component of signal that is linearly independent of the clutter subspace. This letter introduces a detector that augments the GLRT in such a situation by incorporating and capitalizing upon an assumption of homogeneous spread of clutter energy across the dimensions of whole clutter subspace. The receiving operator characteristic curves show how the advantage of proposed detector over GLRT with a heuristic weight choice.

Among all types of unwanted signals in high-frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars, radio-frequency interference (RFI) is dominant since HF band is shared by many radio services. In observation data, there are two types of common RFI. The most common one is the conventional RFI which presents vertical stripe paralleling to range axis in range-Doppler spectrum (RDS) and has been exhaustively reported by previous papers. Meanwhile, a new type of RFI characterized by sloping stripes (called RFISS) in RDS is also frequently observed. This work concentrates on the new RFISSand establishes a unified model for the above two types of RFI. Based on this generalized model, a time-domain RFI suppression algorithm is proposed here. Benefiting from a closed-form approximate maximum likelihood estimator, the proposed algorithm exhibits excellent performance and is computationally efficient. Its operational performance is evaluated using the field data recorded by experimental HFSW OTH radar of Wuhan University.

The performance of high-frequency (HF) surface wave radar degrades significantly due to cochannel interferences (CCIs) in the user-congested HF band (3-30 MHz). Most of the conventional techniques generally follow a simple spatial adaptive processing (SAP) only in the presence of spatially structured interference, but sometimes, we cannot rely on SAP to cancel interference received through the main beam. In this letter, the correlation function of CCI applied in range domain is analyzed, and a new range adaptive processing technique is utilized to suppress the CCI. Real and simulated data results are presented that the general and robust algorithm can achieve effective CCI suppression without comprising the target detection.

The paper analyzes the characteristics of cochannel interference (CCI) in the high-frequency (HF) surface wave radar (HFSWR), which adopts the linear frequency modulated interrupted continuous wave (FMICW). CCI will influence all the range bins, including all the positive and negative frequencies, and the negative frequency range bins contain only the external interference information. Based on the above characteristics, we introduce a new adaptive coherent side-lobe cancellation (CSLC) algorithm based on subarrays that use the negative frequency range bin samples to estimate the interference covariance matrix and correlation vector. Experimental results confirm that the general and robust algorithm can achieve effective CCI suppression using the data recorded by the Ocean State Monitor and Analysis Radar (OSMAR2003, manufactured in 2003), located near Zhoushan in Zhejiang, China.