Transmitters' locations are usually assumed to be known in passive radars that exploit the third-party radio sources as illuminators of opportunity to detect targets of interest. However, there are such cases where transmitters' locations are unavailable in advance, causing difficulties for target localization. To address the problem, this paper investigates a novel transmitter localization method that combines the decoded automatic dependent surveillance-broadcast (ADS-B) information and passive radar measurement information. The localization model is established at the first, followed by the discussion of localization uniqueness and accuracy. The feasibility of the proposed method is further verified by numerical analyses.
The unwanted radar echoes from the ionosphere are collectively called ionospheric clutter. This has proved to be the greatest impediment to achieving consistently good performance in long-range sea state monitoring for high-frequency surface wave radar (HFSWR). Ionospheric clutter can mask sea echoes having similar Doppler shifts. The potential for exploiting some fundamental characteristics of ionospheric clutter in directivity and frequency to suppress it is assessed. A post-Doppler spatial adaptive processing algorithm (PD-SAP) and the dual-frequency operation mode are introduced to suppress directional and non-directional ionospheric clutter, respectively. Experimental results confirm that both of these techniques can achieve effective ionospheric clutter suppression using the field experimental data recorded by the HFSWR OSMAR2003 (Ocean State Monitor and Analysis Radar, manufactured in 2003), located near Zhoushan in Zhejiang, China.
China Digital Radio (CDR) broadcasting is a new standard of digital audio broadcasting of FM frequency (87108 MHz) based on our research and development efforts. It is compatible with the frequency spectrum in analog FM radio and satisfies the requirements for smooth transition from analog to digital signal in FM broadcasting in China. This paper focuses on the signal characteristics and processing methods of radio-based passive radar. The signal characteristics and ambiguity function of a passive radar illumination source are analyzed. The adverse effects on the target detection of the side peaks owing to cyclic prefix, the Doppler ambiguity strips because of signal synchronization, and the range of side peaks resulting from the signal discontinuous spectrum are then studied. Finally, methods for suppressing these side peaks are proposed and their effectiveness is verified by simulations.
As a potential alternative for low-altitude surveillance, the long term evolution-based passive radar (LTEPR) has received worldwide attention due to the wide availability and large bandwidth of LTE. The multiple input multiple output (MIMO) and orthogonal frequency division multiple access (OFDMA) techniques of LTE mean that the radio resource is randomly allocated and multiple signals are transmitted via multiple antennas. The received reference signal of LTEPR is the mixture of multiple signals and discontinous over time. Target SNR loss easily happens if this reference signal is used in matched filtering. However, open publications rarely consider to fractionate reference signal, that is, separating and refining multiple transmitting signals meanwhile dealing with MIMO and OFDMA. This article proposes a complete procedure of reference signal fractionation for LTEPR, in which a heuristic channel estimator is proposed to solve estimation error caused by virtual subcarriers (VSs), and a supervised strategy is proposed to identify the active radio resource from noise. The MIMO technique is decoded after channel equalization. The numerical simulations prove that the heuristic channel estimator is unbiased and the supervised strategy largely reduces the false alarm and missed alarm caused by OFDMA. The real data tests prove that the proposed procedure is feasible and practical to recover multiple transmitting signals.
In air target tracking with a distributed passive radar network, each local node will track the target separately. For two-dimensional (2-D) passive radar, the target’s altitude is often ignored due to the lack of elevation measurement, leading to an estimation deviation of the target state and thereby reducing the subsequent track fusion accuracy. This article proposes a three-dimensional (3-D) target tracking and fusion method for a distributed heterogeneous network with 2-D and 3-D passive radars. The proposed method solves two problems: associating 2-D tracks with 3-D tracks and fusing them. First, we transform the 2-D and 3-D tracks into a common bistatic coordinate and then perform the track association. Second, we obtain equivalent measurements by recalculating Jacobian matrices and measurement predictions of associated local tracks. Then, the global tracks are updated using the equivalent measurements, which have equal state estimation accuracy as centralized fusion. Monte Carlo simulations also demonstrate the equivalence of the estimation accuracy. Moreover, we verify the effectiveness of the proposed algorithm using the field experiment data of a 2-D–3-D passive radar network.
The conventional calibration methods only took into account the mutual coupling (MC) and the gain/phase error. However, the antenna element pattern could also be an important factor in calibration as the antenna element pattern could be direction-dependent. This letter proposes a calibration method to jointly estimate array errors and antenna element patterns for the uniform circular array (UCA). The array errors are composed of MC and gain/phase error. The antenna patterns of different elements are presumed rotationally similar due to the structure of the UCA. The simulation and field experiments demonstrated the effectiveness of the proposed method.
Bowel sounds can reflect the movement and health status of the gastrointestinal tract. However, the traditional manual auscultation method has subjective deviation and is time-consuming and labor-intensive. In order to better assist doctors in diagnosing bowel sounds and improve the reliability and efficiency of bowel sound detection, this study proposed a deep neural network model that combines a residual neural network (ResNet), a bidirectional long short-term memory network (BiLSTM), and an attention mechanism. Firstly, a large number of labeled clinical data was collected using the self-developed multi-channel bowel sound acquisition system, and the multi-scale wavelet decomposition and reconstruction method was used to preprocess the bowel sounds. Then, log Mel spectrogram features were extracted and sent to the network for training. Finally, the performance and effectiveness of the model were evaluated and verified by 10-fold cross-validation and an ablation experiment. The experimental results showed that the precision, recall, and
To deal with the problem of sensor registration in distributed sensors, a common method is to construct pseudo-measurements only related to the bias vector, and then estimate the bias through these pseudo-measurements. Consider the scene of limited communication, a new method to construct the bias pseudo-measurement only by operating the local tracks, covariances, and the equivalent bias measurement matrices is proposed in this paper. Compared with the existing methods, the proposed method can save about 36.4% of the communication cost with other performance unchanged in the system exemplified in this paper.
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