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The issue of adaptive detection is considered for the colocated multi-input multi-output (MIMO) radar in this paper. Meanwhile, the background is partially homogeneous environments (PHE), where the power mismatch is present between the training data and test data. The training data in PHE are utilized to derive effective detectors on the basis of generalized likelihood ratio test, Rao, Wald, Gradient, and Durbin tests. Since the Durbin test-based detector coincides with the Rao test-based detector, the two-step design approach for the Durbin test is used for deriving the new detector. The outcomes of simulation experiments illustrate that the proposed detectors achieve superior effectiveness to existing approaches. Furthermore, the results also show that when the signal mismatch exists, the Wald and Durbin tests maintain robust characteristics. Meanwhile, the Rao test ensures selective property. [ABSTRACT FROM AUTHOR]

• A new decision tree with preference is proposed to build a feature-based detector for small target detection in sea clutter. • Novel splitting and pruning processes are developed to meet the unbalanced demands of error probabilities and samples in radar target detection. • The proposed detector breaks the dimension limitation and has good performance and generalizability. It is difficult for maritime high-resolution radars to realize the small target detection in sea clutter, due to weak target returns and complicated clutter characteristics. Cooperation of multiple features is a recognized way to distinguish target returns from clutter. Therefore, it becomes crucial to build a detector, a special classifier, with unbalanced training samples, i.e., ergodic clutter versus non-ergodic target samples. In this paper, a preferential decision tree (pre-decision tree) with the oblique stopping criterion is proposed, where a preferential Gini index (pre-Gini index) is defined to replace the Gini index and considers rigorous false alarm rates and tolerable missed probabilities for radar target detection. Then, an improved pruning is added to the pre-decision tree to generate a dominant clutter tree, which can accurately control the false alarm rate. The two-step decision is based on the anomaly detection framework and solves the unbalance of the training samples. The proposed method can work in the high-dimensional space directly, and its decision only involves linear operations. The experimental results on the recognized IPIX and CSIR databases illustrate that the proposed method performs well among the available feature-based detectors. [ABSTRACT FROM AUTHOR]

In urban environments, the challenges brought about by multipath propagation are significant for traditional adaptive radar detection problems. To tackle this issue, a generalized multipath signal model combined with environmental geometric prior information (EGPI) is developed. This model can be utilized regardless of whether the multipath signals are fully, partially, or partially-resolvable in the range domain. During the process of designing a detector using the criteria of the one-step generalized likelihood ratio test (GLRT), an attempt to find a closed-form expression for the maximum likelihood estimator of the unknown parameters was unsuccessful. However, by utilizing an adaptive configuration for the initial value, a cyclic optimization algorithm was able to obtain the estimated value within just a few iterations, demonstrating a rapid convergence rate. Furthermore, a detector based on the two-step GLRT criteria is proposed. Compared to traditional detectors, the newly developed detectors make more efficient use of the energy in multipath signals, resulting in improved detection performance. It has been demonstrated that when the whitened angles between subspaces are set to two specific values, the GLRT detectors exhibit the Constant False Alarm Probability (CFAR) property. Simulation results show that the false alarm probability(P f a) at these two specific angles provides upper and lower bounds of P f a. • Deal with the partially resolvable multipath signals in range domain. • The superior detection performance than conventional detector. • The false alarm rate of the detector is only related to principle angles of signal subspaces. [ABSTRACT FROM AUTHOR]

In contemporary security detection systems, the utilization of millimeter-wave radar has assumed a central role owing to its non-contact and innocuous nature. This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection. We identify a prevalent drawback in existing detectors, specifically the adoption of strided convolution or pooling layers, leading to a loss of crucial details that adversely impacts the detection rate. To overcome this limitation, we introduce a novel convolutional structure, termed Wavelet-Conv, which maintains information integrity while segregating features from high and low frequency bands, effectively replacing the unfavorable design. Furthermore, we harness the wavelet transform to enhance channel and spatial attention mechanisms, enabling more effective utilization of frequency band features and ensuring interpretability in the computational process. In this pursuit, we integrate the proposed Wavelet-Conv and Wavelet-Attention modules into the YOLOv8 framework, culminating in a unified model, termed Wavelet-YOLO. Through rigorous experimental validation on two AMMW datasets, our approach exhibits superior performance by significantly enhancing the recall and mean average precision (mAP) of small targets in AMMW images, while maintaining competitive inference speed. Extensive experiments demonstrate the outperformance of our proposed method over existing state-of-the-art approaches. • Enhancing object detection in Active Millimeter Wave Images. • Utilizing DWT for feature dimensionality reduction in convolutional networks. • Leveraging wavelet transform for enhanced spatial and channel attention. • Improving small target detection in blurred images with wavelet-based approaches. [ABSTRACT FROM AUTHOR]

The target localization performance is not only affected by the measurement noise, but also related with the sensor error such as sensor position error and measurement biases. If not properly estimated and calibrated, sensor error may cause large localization error or ghost tracks. This paper investigates the sensor error calibration problem with imperfect calibrators whose positions are not accurately known when both the sensor position error and measurement biases exist. The performance loss without considering the sensor error is analyzed by evaluating the mean square error (MSE). A closed-form solution based on weighted least squares (WLS) is proposed to estimate the sensor measurement bias and update the sensor position by using the biased range and angle measurement about calibrators. Moreover, the optimal geometry for calibration objects is analyzed and the optimum criterions for updating sensor position are proposed by minimizing the trace of the CRLB. The optimal calibrator-sensor geometries in the presence of measurement bias are generally different from the optimal calibrator-sensor geometries in the absence of measurement bias. Finally the performance of the proposed solution is verified and the optimal calibrator-sensor geometry results for 2 or 3 sensors are solved efficiently by using Nelder–Mead simplex method in simulation. • Formulation of sensor error calibration problem using imperfect calibrators. • Theoretical analysis of the performance loss without considering the sensor errors. • Constructing closed-form solution based on WLS to calibrate sensor errors. • Formulation and analysis of the optimal geometry for calibration objects. • Demonstrating performance improvement of the proposed solution and propositions. [ABSTRACT FROM AUTHOR]