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A compressive sensing and processing interacting multiple model (CSP-IMM) filter is proposed in this work. It can track the target directly in compressive domain without signal recovery. Due to the sub-sampled nonlinear measurement, traditional detection before tracking(DBT) strategy is failed in CSP. In this work, the CSP-IMM filter utilizes a DBT method to track a single target. It first adopts a compressive detector to detect the target state by processing the compressive measurement directly, then employs a modified IMM filter for target tracking, this interactive detection and tracking makes the most of the spatial correlation of the target positions in radar sequence signals. Simulation results verify the effectiveness of the proposed method both in target detecting and tracking.

Track before detect(TBD) is an effective target tracking strategy in Nyquist sensing and processing(NSP) field. However, it cannot be directly applied in compressive sensing and processing (CSP), which refers acquiring and processing signal at sub-Nyquist rate without signal recovery, due to the sub-sampled nonlinear measurement. In this work, a TBD method is proposed for tracking a single target in CSP. A pre-tracking compressive detector is first developed to improve the detection performance by processing the compressive measurement directly with prior information on the signal structure, then a Modified Variational Bayesian Adaptive Kalman Filter (VBAKF) algorithm is adopted for target tracking. Simulation results verify the validity of the proposed method both in target detecting and tracking.

The aims of this study were to evaluate the diagnostic value of adenosine thallium-201 myocardial perfusion imaging and to compare it with exercise stress thallium-201 myocardial perfusion imaging for detecting coronary artery disease (CAD) at an early stage. Forty-one patients suspected with CAD were randomly divided into two groups. In Group 1 (n=21) adenosine stress was undertaken; the exercise stress myocardial perfusion imaging was performed in Group 2 (n=20). Coronary angiography (CAG) was performed in each patient within 2 weeks before or after single photon emission computed tomography (SPECT). Adenosine stress group vs. exercise stress group, the sensitivity was 92.86% vs. 100.0%, specificity 57.14% vs. 60.0%, positive predictive value 81.25% vs. 71.43%, negative predictive value 80.0% vs. 100.0%, accuracy 80.95% vs. 80.0% respectively. Detection rates of vessels of coronary artery lesions were 66.67% in Group 1 and 72.22% in Group 2 (P> 0.05). The side effects were mild and transient. Our results demonstrated that adenosine stress myocardial perfusion imaging is a safe and reliable diagnostic method for an early stage of CAD. As a comparative sensitivity and accuracy with exercise stress thallium-201 myocardial perfusion imaging, adenosine stress testing may provide a feasible alternative pharmacological stress method in myocardial SPECT for detection of CAD.

In this paper, we focus on sparsity-based space-time adaptive processing (STAP) in airborne radar with compressive sampling both in Doppler and spatial domains. Compared with the uniform pluses repetition Frequency (UPRF) and uniform arrays (UA) radar, the designed radar transmits random pulse repetition interval pulses and receives the returns with random arrays, which reduces the number of pulses in one coherent processing interval (CPI) and brings down the number of sensors. Firstly, we build the sparse model with spatial-temporal compressive sampling, and analyze the restricted isometry property (RIP) for the steering dictionary. Then, we recover the clutter angle-Doppler profile via existing sparse recovery algorithms, and design the space-time filter to mitigate the clutter. Simulations are conducted to illustrate the effectiveness of the sparsity-based STAP in random PRF and random arrays radar.

In this paper, a novel method based-on Doppler compressive sampling for the sparsity-based space-time adaptive processing (STAP) is proposed. In our method, the radar transmits a small amount of pulses randomly extracted from the aperiodic pulse sequence. Then, we build the sparse model with Doppler compressive sampling, analyze the capability of solving the Doppler ambiguity, and generate the angle-Doppler image of both targets and clutter for a range bin of interest. Simulation results illustrate that the phenomenon of Doppler ambiguity disappear and the recovery performance of proposed method can approach to that of under uncompressed sampling.