The generalized direction detection (GDD) problem involves determining the presence of a signal of interest within matrix-valued data, where the row and column spaces of the signal (if present) are known, but the specific coordinates are unknown. Many detectors have been proposed for GDD, yet there is a lack of analytical results regarding their statistical detection performance. This paper presents a theoretical analysis of two adaptive detectors for GDD in scenarios with known spatial steering vectors. Specifically, we establish their statistical distributions and develop closed-form expressions for both detection probability (PD) and false alarm probability (PFA). Simulation experiments are carried out to validate the theoretical results, demonstrating good agreement between theoretical and simulated results.

In this paper, we propose an effective method for mismatch removal, termed as graph neighborhood motion consensus, to address the feature matching problem which plays a pivotal role in various computer vision tasks. In our method, we convert each feature correspondence into a motion field sample and model it with the probabilistic graphical model (PGM). To differentiate mismatches from true matches, we firstly design a metric based on neighborhood topology consensus and neighborhood interaction to evaluate the correctness of each match. We also design a variance-based similarity search module to make the information used more reliable for better matching performance. To derive the solution of PGM, we build a model to transform the problem into an integer quadratic programming problem and obtain its closed-form solution with linear time complexity. Extensive experiments on general feature matching, fundamental matrix estimation and image registration tasks demonstrate that our proposed method can achieve superior performance over several state-of-the-art approaches.

Abstract For adaptive multichannel radar detection in the framework of multiple alternative hypotheses, where either the target or jamming could be present, a kind of two‐stage detector and classifier is proposed. Precisely, in the first stage, a decision is made on whether a target or a jamming exists. In the second stage, the decision is determined whether it is a target or a jamming. The detector is chosen as subspace‐based adaptive matched filter (SAMF) or adaptive energy detector (AED), while the classifier is selected as the subspace‐based adaptive beamformer orthogonal rejection test (SABORT), whitened SABORT (W‐SABORT), or orthogonal subspace‐based generalised likelihood ratio test (OSGLRT). Among these detectors and classifiers, the OSGLRT is proposed specially for classification in the scheme. Numerical experiments indicate that the proposed methods can achieve better detection and classification performance.

In this letter, we consider the problem of detecting a signal in a kind of nonhomogeneity environment caused by random unknown interference. We propose an effective detector according to the detector design criterion of the two-step Durbin test or the two-step Wald test. The simulation results show that the proposed detector can provide slightly superior performance than that of existing detectors while maintaining approximately the same computational load, both under hypothetical interference conditions and other interference conditions.

This letter investigates the problem of distributed target detection with signal mismatch in homogeneous environment. Precisely, the actual signal steering vector is not aligned with the nominal one which is assumed by radar system. Within this framework, we propose a tunable detector which can adjust its directivity (robustness or selectivity) flexibly and effectively by changing a tunable parameter. Moreover, this tunable detector encompasses the generalized Kelly's generalized likelihood ratio test (GKGLRT) and the generalized adaptive matched filter (GAMF) as its two special cases. Finally, to test the effectiveness of this detector, the real data received by the IPIX radar are used for experiments. The results illustrate the superiority of the proposed detector both in simulation environment and realistic environment.