Search

Towed array sonar are extensively used in a variety of naval, marine and seismological applications for its greater detection performance brought by larger array aperture comparing with other arrays like a hull mounted cylinder array. However, tow platforms such as navy boats often maneuver to avoid other vessels or for other tactical reasons. It is also possible for ocean currents and hydrodynamics to affect the heading therefore shape of the array. Under these conditions, heading of the array will differ from that of the boat, which will cause a great performance degradation of target localization. A commonly used technique to solve the problem is to measure the non-acoustic characteristics such as heading, depth, roll of the array directly through non-acoustic sensors embedded in the array. While non-acoustic sensors will add extra cost and don't work well sometimes. In this paper, a method for estimating the heading of the towed array without any extra sensor modules is proposed. When the sonar works in active mode and a pulsed single-frequency signal is transmitted, the angle difference between heading of the array and the boat can be estimated by processing of the reverberation data without using nonacoustic information. This article will give the results of simulation and trial data analysis, which effectively improves the ability of the towed array to detect weak targets and distinguish between left and right side resolution.

The aim of this study was to elucidate the effects of different machine-harvested cotton-planting patterns on defoliation, yield, and fiber quality in cotton and to provide support for improving the quality of machine-harvested cotton. In the 2015 and 2016 growing seasons, the Xinluzao 45 (XLZ45) and Xinluzao 62 (XLZ62) cultivars, which are primarily cultivated in northern Xinjiang, were used as study materials. Conventional wide-narrow row (WNR), wide and ultra-narrow row (UNR), wide-row spacing with high density (HWR), and wide-row spacing with low density (LWR) planting patterns were used to assess the effects of planting patterns on defoliation, yield, and fiber quality. Compared with WNR, the seed cotton yields were significantly decreased by 2.06–5.48% for UNR and by 2.50–6.99% for LWR, respectively. The main cause of reduced yield was a reduction in bolls per unit area. The variation in HWR yield was −1.07–1.07% with reduced bolls per unit area and increased boll weight, thus demonstrating stable production. In terms of fiber quality indicators, the planting patterns only showed significant effects on the micronaire value, with wide-row spacing patterns showing an increase in the micronaire values. The defoliation and boll-opening results showed that the number of leaves and dried leaves in HWR was the lowest among the four planting patterns. Prior to the application of defoliating agent and before machine-harvesting, the numbers of leaves per individual plant in HWR were decreased by 14.45 and 25.00% on average, respectively, compared with WNR, while the number of leaves per unit area was decreased by 27.44 and 36.21% on average, respectively. The rates of boll-opening and defoliation in HWR were the highest. Specifically, the boll-opening rate before defoliation and machine-harvesting in HWR was 44.54 and 5.94% higher on average than in WNR, while the defoliation rate prior to machine-harvesting was 3.45% higher on average than in WNR. The numbers of ineffective defoliated leaves and leaf trash in HWR were the lowest, decreased by 33.40 and 32.43%, respectively, compared with WNR. In conclusion, the HWR planting pattern is associated with a high and stable yield, does not affect fiber quality, promotes early maturation, and can effectively decrease the amount of leaf trash in machine-picked seed cotton, and thus its use is able to improve the quality of machine-harvested cotton.

The relationship between chlorophyll (Chl) content, leaf nitrogen content (LNC) and red edge parameters were analyzed, and diagnose models of spectra red edge parameters were established for cotton leaf infected by Verticillium wilt. Results showed that: (1) Chl a, Chl b, Chl a+b content, and LNC decreased with increasing in severity level (SL) of Verticillium wilt in cotton leaves, in which Chl a showed the highest and Chl b showed the lowest decrement rate, respectively. (2) Spectrum reflectance increased with increasing severity of Verticillium wilt in the visible region (400–700 nm), near-infrared region (700–1300 nm) and short infrared region (1300–2500 nm), and significantly higher increment was detected in 525–680 nm region (P0.01). Spectrum absorption decreased significantly with increasing SL of Verticillium wilt in the visible region and short infrared region (P0.01), and which increased first and then decreased in near-infrared region. (3) Decrease of REP, Dr, Lo, Depth672, Area672 and increase of Lwidth was detected among red edge parameters, in which Area672 showed the highest and Dr showed the Lowest decrement rate, respectively. (4) There was significantly positive correlation between Chl a, Chl b, Chl a+b, LNC of cotton leaves and REP, Lo, Depth672, Area672 of red edge parameters, significantly negative correlation was found for Lwidth of red edge parameter, while no significant correlation was found for Dr of red edge parameter. (5) Diagnose models of Chl a, Chl a+b,edge parameter, while no significant correlation was found for Dr of red edge parameter. (5) Diagnose models of Chl a, Chl a+b, and LNC for Verticillium wilt in cotton leaves with the independent variables Area672, and Chl b with the independent variables Lo reached the best estimated precision (P0.01). This could diagnose severity level of Verticillium wilt in cotton leaves effectively.