Discriminative correlation filter (DCF)-based methods applied for UAV object tracking have received widespread attention due to their high efficiency. However, these methods are usually troubled by the boundary effect. Besides, the violent environment variations severely confuse trackers that neglect temporal environmental changes among consecutive frames, leading to unwanted tracking drift. In this letter, we propose a novel DCF-based tracking method to promote the insensitivity of the tracker under uncertain environmental changes. Specifically, a regularization term is proposed to learn the environment residual between two adjacent frames, which can enhance the discrimination and insensitivity of the filter in fickle tracking scenarios. Further, we design an efficient strategy to acquire the environment information based on the current observation without additional computation. Exhausted experiments are conducted on two well-known UAV benchmarks, i.e., UAV123_10fps and DTB70. Results verify that the proposed tracker has comparable performance with other 22 state-of-the-art trackers while running at ~ 53 FPS on a low-cost CPU.

In recent years, discriminative correlation filter (DCF)-based trackers have made considerable progress and drawn widespread attention in the unmanned aerial vehicle (UAV) tracking community. Most existing trackers collect historical information, e.g., training samples, previous filters, and response maps, to promote their discrimination and robustness. Under UAV-specific tracking challenges, e.g., fast motion and view change, variations of both the target and its environment in the new frame are unpredictable. Interfered by future unknown environments, trackers that trained with historical information may be confused by the new context, resulting in tracking failure. In this paper, we propose a novel future-aware correlation filter tracker, i.e., FACF. The proposed method aims at effectively utilizing context information in the new frame for better discriminative and robust abilities, which consists of two stages: future state awareness and future context awareness. In the former stage, an effective time series forecast method is employed to reason a coarse position of the target, which is the reference for obtaining a context patch in the new frame. In the latter stage, we firstly obtain the single context patch with an efficient target-aware method. Then, we train a filter with the future context information in order to perform robust tracking. Extensive experimental results obtained from three UAV benchmarks, i.e., UAV123_10fps, DTB70, and UAVTrack112, demonstrate the effectiveness and robustness of the proposed tracker. Our tracker has comparable performance with other state-of-the-art trackers while running at ∼49 FPS on a single CPU.

A novel generalized nested multiple-input multiple-output (MIMO) radar for direction of arrival (DOA) estimation is proposed in this paper. The proposed structure utilizes the extended two-level nested array (ENA) as transmitter and receiver and adjusts the interelement spacing of the receiver with an expanding factor. By optimizing the array element configuration, we can obtain the best number of elements of the transmitter and receiver and the attainable degrees of freedom (DOF). Compared with the existing nested MIMO radar, the proposed MIMO array configuration not only has closed-form expressions for sensors’ positions and the number of maximum DOF, but also significantly improves the array aperture. It is verified that the sum-difference coarray (SDCA) of the proposed nested MIMO radar can get higher DOF without holes. MUSIC algorithm based on Toeplitz matrix reconstruction is employed to prove the rationality and superiority of the proposed MIMO structure.

In this letter, an enhanced coprime array with compressed inter‐element spacing is designed for direction of arrival estimation. First, we analyse and conclude that the 0th sensor of coprime array with compressed inter‐element spacing is redundant for the consecutive lags, thus it is rearranged to the negative part to obtain enhanced coprime array with compressed inter‐element spacing. Then, on the basis of discussing the effect of the shift sensor on the difference co‐array, the number of unique lags and the range of consecutive lags are derived. Theoretical properties show that the closed‐form expressions of coprime array with compressed inter‐element spacing and enhanced coprime array with compressed inter‐element spacing are similar, while more difference lags can be generated from the latter. Moreover, larger inter‐element spacing can slightly alleviate the mutual coupling. Numerical examples verify the advantages of enhanced coprime array with compressed inter‐element spacing in direction of arrival estimation over coprime array with compressed inter‐element spacing configuration and other coprime arrays.